Tootfinder

ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
Jingyu Li, Bozhou Zhang, Xin Jin, Jiankang Deng, Xiatian Zhu, Li Zhang
https://arxiv.org/abs/2508.11428

ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
Autonomous driving requires rich contextual comprehension and precise predictive reasoning to navigate dynamic and complex environments safely. Vision-Language Models (VLMs) and Driving World Models (DWMs) have independently emerged as powerful recipes addressing different aspects of this challenge. VLMs provide interpretability and robust action prediction through their ability to understand multi-modal context, while DWMs excel in generating detailed and plausible future driving scenarios ess…

DriveSimQuest: A VR Driving Simulator and Research Platform on Meta Quest with Unity
Nishanth Chidambaram, Weichen Liu, Manas Satish Bedmutha, Nadir Weibel, Chen Chen
https://arxiv.org/abs/2508.11072

DriveSimQuest: A VR Driving Simulator and Research Platform on Meta Quest with Unity
Using head-mounted Virtual Reality (VR) displays to simulate driving is critical to studying driving behavior and designing driver assistance systems. But existing VR driving simulators are often limited to tracking only eye movements. The bulky outside-in tracking setup and Unreal-based architecture also present significant engineering challenges for interaction researchers and practitioners. We present DriveSimQuest, a VR driving simulator and research platform built on the Meta Quest Pro and…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Jiayue Jin, Lang Qian, Jingyu Zhang, Chuanyu Ju, Liang Song
https://arxiv.org/abs/2508.11453 ht…

EvoPSF: Online Evolution of Autonomous Driving Models via Planning-State Feedback
Recent years have witnessed remarkable progress in autonomous driving, with systems evolving from modular pipelines to end-to-end architectures. However, most existing methods are trained offline and lack mechanisms to adapt to new environments during deployment. As a result, their generalization ability diminishes when faced with unseen variations in real-world driving scenarios. In this paper, we break away from the conventional "train once, deploy forever" paradigm and propose EvoPSF, a nove…

Berlin-based Motor Ai, which develops neuroscience-driven Level 4 autonomous driving tech and plans to deploy it on German roads in 2025, raised a $20M seed (Dean Takahashi/GamesBeat)
https://gamesbeat.com/after-8-years-motor-ai-raises-20m-for-autonom…

After 8 years, Motor Ai raises $20M for autonomous driving software
After eight years of work, Motor Ai has raised $20 million for autonomous driving software that prioritizes compliance.

Can’t pay, won’t pay: impoverished streaming services are driving viewers back to piracy https://www.theguardian.com/film/2025/aug/14/cant-pay-wont-pay-impoverished-streaming-service…

"Climate change-driven heat extremes are driving ‘staggering’ decline in tropical birds, study warns"
#Climate #ClimateChange #Birds

Extreme heat is driving a ‘staggering’ decline in tropical birds
Using climate attribution science, researchers have linked rising heat to declining bird populations around the world.

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao, Xinrui Lin, Yu Zhang, Jianmin Ji, Yanyong Zhang
https://arxiv.org/abs/2509.12207

UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary nature of these autonomous systems and closed-source GPU drivers hinder fine-grained control over GPU executions, often resulting in missed deadlines that compromise vehicle performance. To address this, we present UrgenGo, a non-intrusive, urgency-aware GPU scheduling system that operates without access to application sou…

Coherent Driving of a Quantum System with Modulated Free-Space Electrons
Matthias Kolb, Thomas Spielauer, Thomas Weigner, Giovanni Boero, Dennis R\"atzel, Philipp Haslinger
https://arxiv.org/abs/2509.13904

Coherent Driving of a Quantum System with Modulated Free-Space Electrons
Control of quantum systems typically relies on the interaction with electromagnetic radiation. In this study, we experimentally show that the electromagnetic near-field of a spatially modulated freespace electron beam can be used to drive spin systems, demonstrating free-electron-bound-electron resonant interaction. By periodically deflecting the electron beam of a scanning electron microscope in close proximity to a spin-active solid-state sample, and sweeping the deflection frequency across t…

Week Without Driving
> "If you can drive or afford a car, you may not understand what it’s like to rely on walking, rolling, transit and asking for rides. But for nearly a third of people living in the United States – people with disabilities, young people, seniors and people who can’t afford cars or gas – this is our every day."
Monday, September 29 – Sunday, October 5, 2025

Week Without Driving
Sep 29th – Oct 5th, 2025 - An experience created for everyone to learn firsthand about barriers and challenges for nondrivers. Join, support, or become a host.

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Zhihao Zhang, Chengyang Peng, Minghao Zhu, Ekim Yurtsever, Keith A. Redmill
https://arxiv.org/abs/2509.13132

An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
Autonomous driving in dense, dynamic environments requires decision-making systems that can exploit both spatial structure and long-horizon temporal dependencies while remaining robust to uncertainty. This work presents a novel framework that integrates multi-channel bird's-eye-view occupancy grids with transformer-based sequence modeling for tactical driving in complex roundabout scenarios. To address the imbalance between frequent low-risk states and rare safety-critical decisions, we propose…

Those recurring, intrusive popups in Photoshop and Illustrator are driving me nuts tbh! You’re working and want to get stuff done quickly with a pro tool – but suddenly, your mind needs to process a “useful” tip about how to use the brush tool? WTF. 😂
I haven't found a way to turn them off other than turning off the contextual task bar completely…

The founder and driving force behind the #SaxaVord Spaceport is “stepping back but not down” from his role after receiving a terminal cancer diagnosis: https://www.shetnews.co.uk/2025/07/17/strang-step-back-spaceport-receiving/ - German company Rocket Factory Augsburg (RFA) is confident it could become the first to successfully launch a rocket from the UK’s only licensed vertical spaceport, with the firm planning another test flight during a five-week window at the end of the year.

Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
Bozhou Zhang, Jingyu Li, Nan Song, Li Zhang
https://arxiv.org/abs/2508.11488 https://

Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentiable framework for planning-oriented optimization. We further advance this paradigm through a perception-in-plan framework design, which integrates perception into the planning process. This design facilitates targeted perception guided by evolving planning objectives…

The perfect killing machine doesn’t exist.... https://www.theverge.com/news/708481/microsoft-teams-mercedes-benz-integration-in-car-camera-support

Mercedes-Benz will let you use an in-car camera in Microsoft Teams while driving
Mercedes-Benz is the first car maker to allow its in-car camera to work in Microsoft Teams meetings while you’re driving. It’s coming to the all-new CLA first.

Little-known but Vital: Robert F. Kennedy Jr. is targeting a little-known program that underpins childhood immunizations in the U.S. by paying people who suffer rare side effects from shots.
A Serious Risk: Dramatic changes to the Vaccine Injury Compensation Program risk driving drugmakers from the market, threatening access to shots, experts say.
A Fragile System: The program underscores the fragility of America’s childhood immunization program at a time when Kennedy is renewing d…

RFK Jr. Wants to Revolutionize a Program That Supports Childhood Immunizations. The Results Could Be Catastrophic.
The Vaccine Injury Compensation Program stabilizes the nation’s childhood immunization system while paying those harmed by rare side effects. If the program topples, it could threaten access to vaccines.

Imaginary dialog:
"Excuse me, where is the train station?"
"It's these unmarked doors between the barber shop and the driving school."
#Głogów, #Poland.
#rail

‘As thrilling as driving a sports car’: the Tokyo capsule tower that gave pod-living penthouse chic
https://www.theguardian.com/artanddesign/2025/jul/07/pod-living-nakagin-capsule-tower-tokyo-architectural-marvel-pent…

People talk about how politics shouldn't be like sports. I think it depends a lot on what sports though.
Like, imagine if politicians had to wear the logos of every company they worked stock in and every company that donated to their campaigns like they're driving NASCAR. Of all sports, almost no one wants anything to be more like NASCAR... And yet, when you think about it, there actually *are* some hints we could take from it.
#USPol

EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang, Wenlian Lu, Jianfeng Feng, Taiping Zeng
https://arxiv.org/abs/2507.12263

EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2 mm^3 voxels), these approaches suffer from limited temporal resolution (>0.5 Hz) for tracking rapid neural dynamics during continuous tasks. Conversely, Electroencephalogram (EEG) provides millisecond-scale temporal precision (<=1 ms sampling rate) for real-time guidance of continuous task execution, albeit constrain…

Before earlier this year, I had a Ford Ranger sitting in my driveway that I used three or four times per year. I ordered collector plates for it (since I don't drive it in the winter anyways and I don't want to have to pay to renew plates) and then my sister's car broke down (while she's still owed money on it) so I gave the truck to her. I hadn't even received the collector plates yet. I found out today that she's driving around with the collector plates on it.

TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
Jiawei Wang, Haowei Sun, Xintao Yan, Shuo Feng, Jun Gao, Henry X. Liu
https://arxiv.org/abs/2509.13164

TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
Safe and scalable deployment of end-to-end (E2E) autonomous driving requires extensive and diverse data, particularly safety-critical events. Existing data are mostly generated from simulators with a significant sim-to-real gap or collected from on-road testing that is costly and unsafe. This paper presents TeraSim-World, an automated pipeline that synthesizes realistic and geographically diverse safety-critical data for E2E autonomous driving at anywhere in the world. Starting from an arbitrar…

Monitoring firm MUSO says piracy hit a low in 2020 with 130B website visits and grew by 2024 to 216B; in Sweden, 25% of those surveyed reported pirating in 2024 (Gabriel V Rindborg/The Guardian)
https://www.theguardian.com/film/2025/aug/

Extracting Realistic Pedestrian, Cycling, and Driving Street Networks from OpenStreetMap
https://isprs-archives.copernicus.org/articles/XLVIII-4-W13-2025/103/2025/

Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Arian Mousakhan, Sudhanshu Mittal, Silvio Galesso, Karim Farid, Thomas Brox
https://arxiv.org/abs/2507.13162

Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Existing world models for autonomous driving struggle with long-horizon generation and generalization to challenging scenarios. In this work, we develop a model using simple design choices, and without additional supervision or sensors, such as maps, depth, or multiple cameras. We show that our model yields state-of-the-art performance, despite having only 469M parameters and being trained on 280h of video data. It particularly stands out in difficult scenarios like turning maneuvers and urban …

Using molecular dynamics to investigate the driving force for graphene auto-kirigami
Charlie M. Rawlins, Gareth A. Tribello
https://arxiv.org/abs/2509.13999 https://

Using molecular dynamics to investigate the driving force for graphene auto-kirigami
Experiments [1] have shown that auto-kirigami structures can grow on the surface of graphene because the graphene-graphene adhesion energy is greater than the graphene-substrate interaction. In this work molecular dynamics (MD) simulations of folded graphene both in vacuum and on a substrate have been performed for a range of different initial geometries and at various temperatures. The final equilibrated configuration for many of these simulations resembles a book with a fold that is at the mi…

Low-energy spin waves as potential driving force for superconductivity in electron-doped cuprates
Kristine M. L. Krighaar, Jeppe J. Cederholm, Ellen M. S. Schriver, Henrik Jacobsen, Christine P. Lauritzen, Igor Zaliznyak, C\'edric H. Qvistgaard, Ursula B. Hansen, Ahmed Alshemi, Anton P. J. Stampfl, Jean-Claude Grivel, Dongjoon Song, Kim Lefmann, Machteld E. Kamminga

Low-energy spin waves as potential driving force for superconductivity in electron-doped cuprates
In order to fully utilize the technological potential of unconventional superconductors, an enhanced understanding of the superconducting mechanism is necessary. In the best performing superconductors, the cuprates, superconductivity is intimately linked with magnetism, although the details of this remain elusive. In search of clarity in the magnetism-superconductivity relationship, we focus on the electron-doped cuprate Nd1.85Ce0.15CuO(4-delta) (NCCO). NCCO has an antiferromagnetic ground stat…

MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
Zhipeng Bian, Jieming Zhu, Xuyang Xie, Quanyu Dai, Zhou Zhao, Zhenhua Dong
https://arxiv.org/abs/2509.13773

MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA, a pioneering framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones. With MIRA, users can long-press on images or text objects to receive contextually relevant instruction recommendations for exe…

Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Ngoc Duy Pham, Thusitha Dayaratne, Viet Vo, Shangqi Lai, Sharif Abuadbba, Hajime Suzuki, Xingliang Yuan, Carsten Rudolph
https://arxiv.org/abs/2507.12127

Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Advancements in wireless and mobile technologies, including 5G advanced and the envisioned 6G, are driving exponential growth in wireless devices. However, this rapid expansion exacerbates spectrum scarcity, posing a critical challenge. Dynamic spectrum allocation (DSA)--which relies on sensing and dynamically sharing spectrum--has emerged as an essential solution to address this issue. While machine learning (ML) models hold significant potential for improving spectrum sensing, their adoption …

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Luis Gasco, Hermenegildo Fabregat, Laura Garc\'ia-Sardi\~na, Paula Estrella, Daniel Deniz, Alvaro Rodrigo, Rabih Zbib
https://arxiv.org/abs/2507.13275

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Advances in natural language processing and large language models are driving a major transformation in Human Capital Management, with a growing interest in building smart systems based on language technologies for talent acquisition, upskilling strategies, and workforce planning. However, the adoption and progress of these technologies critically depend on the development of reliable and fair models, properly evaluated on public data and open benchmarks, which have so far been unavailable in t…

Spontaneous formation of subsystem and bath under accordion-type driving
Suyang Lin, Ming Gong, Congjun Wu
https://arxiv.org/abs/2509.12761 https://arxiv.o…

Spontaneous formation of subsystem and bath under accordion-type driving
Floquet modulations often yield effective Hamiltonians not easily accessible in traditional time-dependent systems, which brings opportunities for exploring novel physics of quantum dynamics. We investigate a Floquet system exhibiting translational symmetry at any fixed time but the spatial periodicity is time-dependent. Such a system is a natural platform for studying thermalization and novel dynamical structures. We find that the single-particle Hilbert space spontaneously develops a structur…

Safeguarding Federated Learning-based Road Condition Classification
Sheng Liu, Panos Papadimitratos
https://arxiv.org/abs/2507.12568 https://

Safeguarding Federated Learning-based Road Condition Classification
Federated Learning (FL) has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification (RCC) systems, harnessing distributed sensing, computing, and communication resources on board vehicles without sharing sensitive image data. However, the collaborative nature of FL-RCC frameworks introduces new vulnerabilities: Targeted Label Flipping Attacks (TLFAs), in which malicious clients (vehicles) deliberately alter their trainin…

Riverwest 24 is next weekend. I thought about tossing my bike in the car and driving down to do some laps but then realized it's like 5 miles from my house so I'll just bike down there and do laps, hang out, etc.
https://www.riverwest24.com/

AI Factories: It's time to rethink the Cloud-HPC divide
Pedro Garcia Lopez, Daniel Barcelona Pons, Marcin Copik, Torsten Hoefler, Eduardo Qui\~nones, Maciej Malawski, Peter Pietzutch, Alberto Marti, Thomas Ohlson Timoudas, Aleksander Slominski
https://arxiv.org/abs/2509.12849

AI Factories: It's time to rethink the Cloud-HPC divide
The strategic importance of artificial intelligence is driving a global push toward Sovereign AI initiatives. Nationwide governments are increasingly developing dedicated infrastructures, called AI Factories (AIF), to achieve technological autonomy and secure the resources necessary to sustain robust local digital ecosystems. In Europe, the EuroHPC Joint Undertaking is investing hundreds of millions of euros into several AI Factories, built atop existing high-performance computing (HPC) super…

Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Ruibo Li, Hanyu Shi, Zhe Wang, Guosheng Lin
https://arxiv.org/abs/2509.13116 https://

Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typically consist of mobile foregrounds and static backgrounds, allowing motion understanding to be associated with scene parsing. Based on this observation, we propose a novel weakly supervised paradigm that replaces motion…

We just drive home by train from visiting friends. During the first hour in the train we were just SO glad that we took the train instead of the car. We were just so tired! Driving by car would have simply been dangerous.
So we just took a nap in the train. - wouldn't have been so cool to do that in the car 😉

China's vision for its autonomous vehicle industry, backed by state support and infrastructure investment, is driving its lead in ADAS and robotaxi deployment (Yi-Ling Liu/Rest of World)
https://restofworld.org/2025/china-ai-powered-self-driving-cars/

China’s vision for a driverless future is miles ahead of everyone else’s
Strong government backing and strict regulatory oversight of the Chinese autonomous vehicle industry contrasts sharply with the piecemeal laws and slower adoption in the U.S.

Browns star Myles Garrett cited for driving 100 mph after team's preseason opener

https://www.cbssports.com/nfl/news/browns-star-myles-garrett-ci…

MAP: End-to-End Autonomous Driving with Map-Assisted Planning
Huilin Yin, Yiming Kan, Daniel Watzenig
https://arxiv.org/abs/2509.13926 https://arxiv.org/pd…

MAP: End-to-End Autonomous Driving with Map-Assisted Planning
In recent years, end-to-end autonomous driving has attracted increasing attention for its ability to jointly model perception, prediction, and planning within a unified framework. However, most existing approaches underutilize the online mapping module, leaving its potential to enhance trajectory planning largely untapped. This paper proposes MAP (Map-Assisted Planning), a novel map-assisted end-to-end trajectory planning framework. MAP explicitly integrates segmentation-based map features and …

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints
Xian-Long Lee, Adel N. Toosi, Peter Pudney, Ian McLeod, Muhammad Aamir Cheema, Hao Wang
https://arxiv.org/abs/2507.12877

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints
The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load shifting and grid support, which can reduce both charging costs and grid stress. In this work, we de…

No playing Spanish-language music: Many immigrants say they have new rules for driving : NPR
https://www.npr.org/2025/07/12/nx-s1-5437957/no-playing-spanish-language-music-many-immigrants-say-they-have-new-rules-for-driving

This looks like great fun; big big construction machines driven by AI - that's what we want our AI to be doing, driving huge machines with shovels on.
That must be great fun to debug.
https://bedrockrobotics.com/news/introducing-bedrock-robotics…

Bedrock Robotics

Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning
Ruining Yang, Jingyuan Zhou, Qiqing Wang, Jinhao Liang, Kaidi Yang
https://arxiv.org/abs/2509.12378

Platoon-Centric Green Light Optimal Speed Advisory Using Safe Reinforcement Learning
With recent advancements in Connected Autonomous Vehicles (CAVs), Green Light Optimal Speed Advisory (GLOSA) emerges as a promising eco-driving strategy to reduce the number of stops and idle time at intersections, thereby reducing energy consumption and emissions. Existing studies typically improve energy and travel efficiency for individual CAVs without considering their impacts on the entire mixed-traffic platoon, leading to inefficient traffic flow. While Reinforcement Learning (RL) has the…

Guess they shut down the Fast and Furious in the west end.
https://www.theguardian.com/uk-news/2025/aug/12/police-seize-supercars-worth-more-than-6m-in-crackdown-on-antisocial-driving-in-london

Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?
Riya Gill, Ievgeniia Kuzminykh, Maher Salem, Bogdan Ghita
https://arxiv.org/abs/2509.11438

Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?
This study aims to develop an adaptive learning platform that leverages generative AI to automate assessment creation and feedback delivery. The platform provides self-correcting tests and personalised feedback that adapts to each learners progress and history, ensuring a tailored learning experience. The study involves the development and evaluation of a web-based application for revision for the UK Driving Theory Test. The platform generates dynamic, non-repetitive question sets and offers ad…

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning
Xudong Wang, Hongyang Du, Lei Feng, Kaibin Huang
https://arxiv.org/abs/2507.12910

Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning
The growing demand for low-latency computing in 6G is driving the use of UAV-based low-altitude mobile edge computing (MEC) systems. However, limited spectrum often leads to severe uplink interference among ground terminals (GTs). In this paper, we investigate a rate-splitting multiple access (RSMA)-enabled low-altitude MEC system, where a UAV-based edge server assists multiple GTs in concurrently offloading their tasks over a shared uplink. We formulate a joint optimization problem involving t…

Gravitational Waves from First-Order Phase Transitions Assisted by Temperature-Enhanced Scatterings
Arnab Chaudhuri
https://arxiv.org/abs/2507.13135 https:…

Gravitational Waves from First-Order Phase Transitions Assisted by Temperature-Enhanced Scatterings
Scatterings whose cross sections increase as the cosmic temperature decreases, known as temperature-enhanced scatterings, can have a significant impact on the thermal effective potential of scalar fields responsible for driving cosmological first-order phase transitions. In this work, we systematically investigate how the inclusion of temperature-dependent corrections to the effective potential affects key phase transition parameters, including the nucleation temperature, latent heat release, a…

AirBreath Sensing: Protecting Over-the-Air Distributed Sensing Against Interference
Zhanwei Wang, Mingyao Cui, Huiling Yang, Qunsong Zeng, Min Sheng, Kaibin Huang
https://arxiv.org/abs/2508.11267

AirBreath Sensing: Protecting Over-the-Air Distributed Sensing Against Interference
A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an …

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
Yuhang Lu, Jiadong Tu, Yuexin Ma, Xinge Zhu
https://arxiv.org/abs/2507.12499 https:/…

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising approach to unify perception, prediction, and planning within a single framework, reducing information loss and improving adaptability. However, existing methods often rely on fixed and sparse trajectory supervision, limiting their ability to capture the hierarchical reasoning process that human drivers naturally employ. To bridge this gap, we propose ReAL-AD, a Reasoning-Augmented Learning framework that structures decision-making in aut…

Christianity, Nationalism, and Ideology in a Globalized World
https://ift.tt/tSUZMBE
TOC The Asia Pacific Journal of Anthropology, vol 18, 5 (2017) …
via Input 4 RELCFP https://

Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours
Iniakpokeikiye Peter Thompson, Yi Dewei, Reiter Ehud
https://arxiv.org/abs/2509.05358 …

Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours
This paper presents a unique driving dataset collected in Nigeria via mobile phone sensors to support a machine learning model for detecting alcohol-influenced driving behaviours, with the long-term aim of integrating this model into a mobile application that encourages safer driving behaviours. Driving under the influence of alcohol is a major public safety concern, particularly in low-income countries like Nigeria, where traditional enforcement mechanisms may be limited. The proposed model le…

Scheduling follow-up observations of energetic transients with the neutrino target scheduler (NuTS)
Tobias Heibges, Claire Gu\'epin, Luke Kupari, Hannah Wistrand, Randy Lahm, Johannes Eser, Mary Hall Reno, Tonia M. Venters, Lawrence Wiencke
https://arxiv.org/abs/2509.13844

Scheduling follow-up observations of energetic transients with the neutrino target scheduler (NuTS)
Space missions offer unique opportunities for studying ultra-high-energy (UHE) cosmic rays and neutrinos by leveraging secondary emissions generated by extensive air showers (EAS) resulting from their interactions with the atmosphere or Earth's crust. Detecting UHE neutrinos associated with transient sources holds great potential for unraveling the origins of UHE cosmic rays and the physical processes driving their production. Stratospheric balloon missions, illustrated recently by the Extreme …

So none of these UI designers realize that everyone isn’t upgrading to this whole liquid glass thing and does not bother to have a different icon at hand? All these ugly App Icons with iOS 18 is literally driving me crazy.

HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editing
Tielong Wang, Yuxuan Xiong, Jinfan Liu, Zhifan Zhang, Ye Chen, Yue Shi, Bingbing Ni
https://arxiv.org/abs/2507.11971

HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editing
Current 3D representations like meshes, voxels, point clouds, and NeRF-based neural implicit fields exhibit significant limitations: they are often task-specific, lacking universal applicability across reconstruction, generation, editing, and driving. While meshes offer high precision, their dense vertex data complicates editing; NeRFs deliver excellent rendering but suffer from structural ambiguity, hindering animation and manipulation; all representations inherently struggle with the trade-of…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna, Lars Kunze, Daniele De Martini
https://arxiv.org/abs/2509.13914 http…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (withou…

A molecular rotor driven by an electric field on graphene
Wanxing Lin, Xiaobo Li, Yan-Ling Zhao
https://arxiv.org/abs/2509.13995 https://arxiv.org/pdf/2509…

A molecular rotor driven by an electric field on graphene
We propose a scheme for driving a dipolar molecular rotor to rotate continuously by applying an external electric field: the dipolar rotor is fixed on a graphene sheet via a metal atom to facilitate the free rotation; it is in the meantime subjected to an electric field oriented parallel to the graphene sheet. We use computational modeling with density functional theory and Newtonian mechanics, similar to molecular dynamics simulations, to obtain the torque, angular velocity, and rotation perio…

Magnetoelectric multiferroics: from fundamentals to transformative applications -- a mini review
Micha{\l} Wanic
https://arxiv.org/abs/2507.12867 https://

Magnetoelectric multiferroics: from fundamentals to transformative applications -- a mini review
Multiferroics, combining ferroelectric and magnetic orders, enable magnetoelectric (ME) coupling for advanced applications. This mini review explores single-phase and composite multiferroics, examining phenomenological, microscopic, nanostruc-tured, and quantum mechanisms driving ME effects. Phenomenological models quantify coupling coefficients, while microscopic approaches reveal spin-lattice in-teractions, including frustrated spin states and Dzyaloshinskii-Moriya contributions. Nanostructur…

Cowboys All-Pro arrested for allegedly driving 97 MPH with gun in back seat https://www.pennlive.com/sports/2025/07/cowboys-all-pro-arrested-for-allegedly-driving-97-mph-with-gun-in-back-seat.html

Full Professor Position, of Environmental Biogeochemistry of Metals, at Division of Environmental Geosciences, Uni Vienna (@edge-vienna.bsky.social) #geology #Austria #Österreich

We are hiring: Full Professor of Environmental Biogeochemistry of Metals
Human activities are driving rapid environmental change on a global scale, with pollutants playing a critical role in pushing planetary systems beyond safe limits. These changes have profound consequences for biodiversity, ecosystem functioning, and human health.

A New Mechanism for ULF Wave Modulation of Energetic Electron Precipitation
L. Olifer, D. Zhou, M. Patel, I. R. Mann, M. K. Hudson, A. W. Degeling, C. O. Heinke, G. R. Sivakoff, A. Kale, S. Kasahara, S. Yokota, K. Keika, T. Hori, T. Mitani, T. Takashima, Y. Kasahara, S. Matsuda, A. Shinbori, A. Matsuoka, M. Teramoto, K. Yamamoto, I. Shinohara, Y. Miyoshi
https:…

A New Mechanism for ULF Wave Modulation of Energetic Electron Precipitation
The May 2024 geomagnetic superstorm provided the opportunity to explore how strong wave-particle interactions affect energetic electron precipitation under intense driving. Using coordinated measurements from a balloon-borne Timepix-based X-ray detector, ground-based riometers and magnetometers, and Arase satellite observations, we identified quasi-periodic bursts of energetic electron precipitation coincident with Pc5 ultra low frequency (ULF) wave oscillations. Arase satellite data revealed e…

A look at training sessions hosted by robotaxi companies, like Zoox, to teach first responders how to handle emergencies involving self-driving cars (Lisa Bonos/Washington Post)

A new challenge for emergency workers: Learning how to handle a robotaxi
Police and firefighters are getting trained on how to respond to emergencies involving self-driving cars — or pull them over in a traffic stop.

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Renjie Li, Ruijie Ye, Mingyang Wu, Hao Frank Yang, Zhiwen Fan, Hezhen Hu, Zhengzhong Tu
https://arxiv.org/abs/2507.12463

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Humans are integral components of the transportation ecosystem, and understanding their behaviors is crucial to facilitating the development of safe driving systems. Although recent progress has explored various aspects of human behavior$\unicode{x2014}$such as motion, trajectories, and intention$\unicode{x2014}$a comprehensive benchmark for evaluating human behavior understanding in autonomous driving remains unavailable. In this work, we propose $\textbf{MMHU}$, a large-scale benchmark for hu…

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding
Benjamin Stoler, Juliet Yang, Jonathan Francis, Jean Oh
https://arxiv.org/abs/2507.10749

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding
Safety-critical scenarios are essential for training and evaluating autonomous driving (AD) systems, yet remain extremely rare in real-world driving datasets. To address this, we propose Real-world Crash Grounding (RCG), a scenario generation framework that integrates crash-informed semantics into adversarial perturbation pipelines. We construct a safety-aware behavior representation through contrastive pre-training on large-scale driving logs, followed by fine-tuning on a small, crash-rich dat…

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Siyi Lu, Run Liu, Dongsheng Yang, Lei He
https://arxiv.org/abs/2508.06074

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretability, suffer from error propagation and coordination issues, whereas end-to-end learning systems can simplify the design but face computational bottlenecks. This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced …

Steady-state entanglement of spin qubits mediated by non-reciprocal and chiral magnons
Martijn Dols, Mikhail Cherkasskii, Victor A. S. V. Bittencourt, Carlos Gonzalez-Ballestero, Durga B. R. Dasari, Silvia Viola Kusminskiy
https://arxiv.org/abs/2509.13094

Steady-state entanglement of spin qubits mediated by non-reciprocal and chiral magnons
We propose a hybrid quantum system in which a magnet supporting non-reciprocal magnons, chiral magnons, or both mediates the dissipative and unidirectional coupling of spin qubits. By driving the qubits, the steady state of this qubit-qubit coupling scheme becomes the maximally entangled Bell state. We devise a protocol where the system converges to this entangled state and benchmark it including qubit decay and dephasing. The protocol is numerically tested on a hybrid system consisting of nitr…

DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving
Ana\"is Halin, Christel Devue, Marc Van Droogenbroeck
https://arxiv.org/abs/2508.10618

DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving
The increasing integration of automation in vehicles aims to enhance both safety and comfort, but it also introduces new risks, including driver disengagement, reduced situation awareness, and mode confusion. In this work, we propose the DEV framework, a closed-loop framework for risk-aware adaptive driving automation that captures the dynamic interplay between the driver, the environment, and the vehicle. The framework promotes to continuously adjusting the operational level of automation base…

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
Brian Shing-Hei Wong, Joshua Mincheol Kim, Sin-Hang Fung, Qing Xiong, Kelvin Fu-Kiu Ao, Junkang Wei, Ran Wang, Dan Michelle Wang, Jingying Zhou, Bo Feng, Alfred Sze-Lok Cheng, Kevin Y. Yip, Stephen Kwok-Wing Tsui, Qin Cao
https://arxiv.o…

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation
Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. Th…

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta, Jovin D'sa, Faizan M. Tariq, David Isele, Sangjae Bae
https://arxiv.org/abs/2507.11940

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose …

Bedrock Robotics raised $80M from Eclipse and 8VC to develop retrofit self-driving kits for construction and worksite vehicles, in testing with five companies (Kirsten Korosec/TechCrunch)
https://techcrunch.com/2025/07/16/ex-way…

Ex-Waymo engineers launch Bedrock Robotics with $80M to automate construction | TechCrunch
Former Waymo engineers have launched an autonomous vehicle startup with $80 million in venture funds.

SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
Qingzhao Zhang, Shaocheng Luo, Z. Morley Mao, Miroslav Pajic, Michael K. Reiter
https://arxiv.org/abs/2509.11120

SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
Autonomous vehicles, including self-driving cars, robotic ground vehicles, and drones, rely on complex sensor pipelines to ensure safe and reliable operation. However, these safety-critical systems remain vulnerable to adversarial sensor attacks that can compromise their performance and mission success. While extensive research has demonstrated various sensor attack techniques, critical gaps remain in understanding their feasibility in real-world, end-to-end systems. This gap largely stems from…

Bans Are Driving Up Later Abortions
https://jessica.substack.com/p/bans-are-driving-up-later-abortions

LaViPlan : Language-Guided Visual Path Planning with RLVR
Hayeon Oh
https://arxiv.org/abs/2507.12911 https://arxiv.org/pdf/2507.12911…

LaViPlan : Language-Guided Visual Path Planning with RLVR
Out-of-distribution (OOD) scenarios in autonomous driving refer to situations that deviate from the training domain, often leading to unexpected and potentially hazardous behavior from planners that lack prior exposure to such cases. Recently, Vision-Language Models (VLMs) have been introduced into autonomous driving research for their promising generalization capabilities in OOD settings. Early studies demonstrated that VLMs could recognize OOD scenarios and generate user-level decisions such …

A new wave of climate research is sounding a stark warning:
Human activity may be driving drought more intensely
– and more directly
– than previously understood.
The southwestern United States has been in a historic #megadrought for much of the past two decades,
with its reservoirs including lakes Mead and Powell dipping to record lows
and legal disputes erupting ov…

Climate models reveal how human activity may be locking the Southwest into permanent drought
The drought has been linked to the Pacific Decadal Oscillation, a natural climate pattern. A new study finds global warming is now influencing that natural phenomenon.

Mechanical Squeezed-Fock Qubit: Towards Quantum Weak-Force Sensing
Yi-Fan Qiao, Jun-Hong An, Peng-Bo Li
https://arxiv.org/abs/2507.13161 https://

Mechanical Squeezed-Fock Qubit: Towards Quantum Weak-Force Sensing
Mechanical qubits offer unique advantages over other qubit platforms, primarily in terms of coherence time and possibilities for enhanced sensing applications, but their potential is constrained by the inherently weak nonlinearities and small anharmonicity of nanomechanical resonators. We propose to overcome this shortcoming by using squeezed Fock states of phonons in a parametrically driven nonlinear mechanical oscillator. We find that, under two-phonon driving, squeezed Fock states become eig…

Tokopedia sellers say Tokopedia's strengths have eroded since its TikTok Shop merger in Indonesia, driving thousands of sellers to join rivals, including Toco (Michelle Anindya/Rest of World)
https://restofworld.org/2025/tiktok-indonesia-tokopedia-merger-problems…

TikTok’s messy merger in Indonesia could be a preview of what’s to come in the U.S.
A year after merging with local giant Tokopedia, TikTok’s business model is alienating sellers, forcing many to join rival platforms.

Road Obstacle Video Segmentation
Shyam Nandan Rai, Shyamgopal Karthik, Mariana-Iuliana Georgescu, Barbara Caputo, Carlo Masone, Zeynep Akata
https://arxiv.org/abs/2509.13181 htt…

Road Obstacle Video Segmentation
With the growing deployment of autonomous driving agents, the detection and segmentation of road obstacles have become critical to ensure safe autonomous navigation. However, existing road-obstacle segmentation methods are applied on individual frames, overlooking the temporal nature of the problem, leading to inconsistent prediction maps between consecutive frames. In this work, we demonstrate that the road-obstacle segmentation task is inherently temporal, since the segmentation maps for cons…

Exploring Micro Accidents and Driver Responses in Automated Driving: Insights from Real-world Videos
Wei Xiang, Chuyue Zhang, Jie Yan
https://arxiv.org/abs/2508.07256 https://…

Exploring Micro Accidents and Driver Responses in Automated Driving: Insights from Real-world Videos
Automated driving in level 3 autonomy has been adopted by multiple companies such as Tesla and BMW, alleviating the burden on drivers while unveiling new complexities. This article focused on the under-explored territory of micro accidents during automated driving, characterized as not fatal but abnormal aberrations such as abrupt deceleration and snake driving. These micro accidents are basic yet pervasive events that might results in more severe accidents. Through collecting a comprehensive d…

HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction
Bingqing Wei, Lianmin Chen, Zhongyu Xia, Yongtao Wang
https://arxiv.org/abs/2509.11719

HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction
Multi-agent trajectory prediction in autonomous driving requires a comprehensive understanding of complex social dynamics. Existing methods, however, often struggle to capture the full richness of these dynamics, particularly the co-existence of multi-scale interactions and the diverse behaviors of heterogeneous agents. To address these challenges, this paper introduces HeLoFusion, an efficient and scalable encoder for modeling heterogeneous and multi-scale agent interactions. Instead of relyin…

A Georgia man who had blamed the COVID-19 vaccine for making him depressed and suicidal
has been identified as the shooterwho opened fire late Friday on the U.S. Centers for Disease Control and Prevention headquarters, killing a police officer.
The 30-year-old suspect, who died during the incident,
had also tried to get into the CDC’s headquarters in Atlanta but was stopped by guards before driving to a pharmacy across the street and opening fire.
The man, identified a…

Union wants statement against COVID vaccine misinformation after CDC shooting
A Georgia man who had blamed the COVID-19 vaccine for making him depressed and suicidal has been identified as the shooter who opened fire late Friday on the U.S. Centers for Disease Control and Prevention headquarters, killing a police officer. The 30-year-old suspect, who died during the incident, had also tried to get into the CDC’s headquarters in Atlanta but was stopped by guards before driving to a pharmacy across the street and opening fire, a law enforcement official told The Associat…

Detecting AI Assistance in Abstract Complex Tasks
Tyler King, Nikolos Gurney, John H. Miller, Volkan Ustun
https://arxiv.org/abs/2507.10761 https://…

Detecting AI Assistance in Abstract Complex Tasks
Detecting assistance from artificial intelligence is increasingly important as they become ubiquitous across complex tasks such as text generation, medical diagnosis, and autonomous driving. Aid detection is challenging for humans, especially when looking at abstract task data. Artificial neural networks excel at classification thanks to their ability to quickly learn from and process large amounts of data -- assuming appropriate preprocessing. We posit detecting help from AI as a classificatio…

Gaze-Based Indicators of Driver Cognitive Distraction: Effects of Different Traffic Conditions and Adaptive Cruise Control Use
Ana\"is Halin, Adrien Deli\`ege, Christel Devue, Marc Van Droogenbroeck
https://arxiv.org/abs/2508.10624

Gaze-Based Indicators of Driver Cognitive Distraction: Effects of Different Traffic Conditions and Adaptive Cruise Control Use
In this simulator study, we investigate how gaze parameters reflect driver cognitive distraction under varying traffic conditions and adaptive cruise control (ACC) use. Participants completed six driving scenarios that combined two levels of cognitive distraction (with/without mental calculations) and three levels of driving environment complexity. Throughout the experiment, participants were free to activate or deactivate an ACC. We analyzed two gaze-based indicators of driver cognitive distra…

Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Bozhou Zhang, Nan Song, Bingzhao Gao, Li Zhang
https://arxiv.org/abs/2508.11492 https://

Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Trajectory prediction and planning in autonomous driving are highly challenging due to the complexity of predicting surrounding agents' movements and planning the ego agent's actions in dynamic environments. Existing methods encode map and agent positions and decode future trajectories in Cartesian coordinates. However, modeling the relationships between the ego vehicle and surrounding traffic elements in Cartesian space can be suboptimal, as it does not naturally capture the varying influence …

MoSE: Skill-by-Skill Mixture-of-Expert Learning for Autonomous Driving
Lu Xu, Jiaqian Yu, Xiongfeng Peng, Yiwei Chen, Weiming Li, Jaewook Yoo, Sunghyun Chunag, Dongwook Lee, Daehyun Ji, Chao Zhang
https://arxiv.org/abs/2507.07818

MoSE: Skill-by-Skill Mixture-of-Expert Learning for Autonomous Driving
Recent studies show large language models (LLMs) and vision language models (VLMs) trained using web-scale data can empower end-to-end autonomous driving systems for a better generalization and interpretation. Specifically, by dynamically routing inputs to specialized subsets of parameters, the Mixture-of-Experts (MoE) technique enables general LLMs or VLMs to achieve substantial performance improvements while maintaining computational efficiency. However, general MoE models usually demands ext…

PerchMobi^3: A Multi-Modal Robot with Power-Reuse Quad-Fan Mechanism for Air-Ground-Wall Locomotion
Yikai Chen, Zhi Zheng, Jin Wang, Bingye He, Xiangyu Xu, Jialu Zhang, Huan Yu, Guodong Lu
https://arxiv.org/abs/2509.12620

PerchMobi^3: A Multi-Modal Robot with Power-Reuse Quad-Fan Mechanism for Air-Ground-Wall Locomotion
Achieving seamless integration of aerial flight, ground driving, and wall climbing within a single robotic platform remains a major challenge, as existing designs often rely on additional adhesion actuators that increase complexity, reduce efficiency, and compromise reliability. To address these limitations, we present PerchMobi^3, a quad-fan, negative-pressure, air-ground-wall robot that implements a propulsion-adhesion power-reuse mechanism. By repurposing four ducted fans to simultaneously p…

Google says total organic click volume from Search to websites has been "relatively stable" YoY and it's sending "slightly more quality clicks" than a year ago (Liz Reid/The Keyword)
https://blog.google/products/search/ai-sear…

AI in Search is driving more queries and higher quality clicks
We continue to send billions of clicks to the web every day and are committed to prioritizing the web in our AI experiences in Search.

MultiPark: Multimodal Parking Transformer with Next-Segment Prediction
Han Zheng, Zikang Zhou, Guli Zhang, Zhepei Wang, Kaixuan Wang, Peiliang Li, Shaojie Shen, Ming Yang, Tong Qin
https://arxiv.org/abs/2508.11537

MultiPark: Multimodal Parking Transformer with Next-Segment Prediction
Parking accurately and safely in highly constrained spaces remains a critical challenge. Unlike structured driving environments, parking requires executing complex maneuvers such as frequent gear shifts and steering saturation. Recent attempts to employ imitation learning (IL) for parking have achieved promising results. However, existing works ignore the multimodal nature of parking behavior in lane-free open space, failing to derive multiple plausible solutions under the same situation. Notab…

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Zewei Yang, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.13956

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference…

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He, Yifan Wang, Yue Wang, Ruizhen Hu, Yiyi Liao, Xiaowei Zhou, Hujun Bao
https://arxiv.org/abs/2507.11540

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by high costs, low resolution, and environmental sensitivity, limiting their applicability in real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalizatio…

ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience
Yeana Lee Bond (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Mungyeong Choe (Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, USA), Baker Kasim Hasan (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Arsh Siddiqui (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Myounghoon Jeon (Com…

ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience
Studies on in-vehicle conversational agents have traditionally relied on pre-scripted prompts or limited voice commands, constraining natural driver-agent interaction. To resolve this issue, the present study explored the potential of a ChatGPT-based in-vehicle agent capable of carrying continuous, multi-turn dialogues. Forty drivers participated in our experiment using a motion-based driving simulator, comparing three conditions (No agent, Pre-scripted agent, and ChatGPT-based agent) as a with…

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
Mingyue Lei, Zewei Zhou, Hongchen Li, Jiaqi Ma, Jia Hu
https://arxiv.org/abs/2508.07686

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
End-to-end paradigm has emerged as a promising approach to autonomous driving. However, existing single-agent end-to-end pipelines are often constrained by occlusion and limited perception range, resulting in hazardous driving. Furthermore, their black-box nature prevents the interpretability of the driving behavior, leading to an untrustworthiness system. To address these limitations, we introduce Risk Map as Middleware (RiskMM) and propose an interpretable cooperative end-to-end driving frame…

SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Toktam Mohammadnejad, Jovin D'sa, Behdad Chalaki, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari
https://arxiv.org/abs/2507.10968

SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Merging onto a highway is a complex driving task that requires identifying a safe gap, adjusting speed, often interactions to create a merging gap, and completing the merge maneuver within a limited time window while maintaining safety and driving comfort. In this paper, we introduce a Safe Merging and Real-Time Merge (SMART-Merge) planner, a lattice-based motion planner designed to facilitate safe and comfortable forced merging. By deliberately adapting cost terms to the unique challenges of f…

Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
Mohammed Hassanin, Mohammad Abu Alsheikh, Carlos C. N. Kuhn, Damith Herath, Dinh Thai Hoang, Ibrahim Radwan
https://arxiv.org/abs/2507.11852

Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
The rapid adoption of micromobility solutions, particularly two-wheeled vehicles like e-scooters and e-bikes, has created an urgent need for reliable autonomous riding (AR) technologies. While autonomous driving (AD) systems have matured significantly, AR presents unique challenges due to the inherent instability of two-wheeled platforms, limited size, limited power, and unpredictable environments, which pose very serious concerns about road users' safety. This review provides a comprehensive a…

Crosslisted article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao, Xinrui Lin, Yu Zhang, Jianmin Ji, Yanyong Zhang

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Zhen Tian, Fujiang Yuan, Yangfan He, Qinghao Li, Changlin Chen, Huilin Chen, Tianxiang Xu, Jianyu Duan, Yanhong Peng, Zhihao Lin
https://arxiv.org/abs/2509.07412

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Autonomous driving has attracted great interest due to its potential capability in full-unsupervised driving. Model-based and learning-based methods are widely used in autonomous driving. Model-based methods rely on pre-defined models of the environment and may struggle with unforeseen events. Proximal policy optimization (PPO), an advanced learning-based method, can adapt to the above limits by learning from interactions with the environment. However, existing PPO faces challenges with poor tr…

Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
Yan Gong, Naibang Wang, Jianli Lu, Xinyu Zhang, Yongsheng Gao, Jie Zhao, Zifan Huang, Haozhi Bai, Nanxin Zeng, Nayu Su, Lei Yang, Ziying Song, Xiaoxi Hu, Xinmin Jiang, Xiaojuan Zhang, Susanto Rahardja
https://arxiv.org/abs/2508.07560…

Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and multi-agent collaboration. As autonomous vehicles transition from controlled environments to real-world deployment, ensuring the safety and reliability of BEV perception in complex scenarios - such as occlusions, adverse weather, and dynamic traffic - remains a critical challenge. This survey provides the first comprehens…

RAPTOR: A Foundation Policy for Quadrotor Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
https://arxiv.org/abs/2509.11481 https://arxiv.org/pdf/250…

RAPTOR: A Foundation Policy for Quadrotor Control
Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies trained using Reinforcement Learning (RL), are highly specialized for single environments. Because of this overfitting, they are known to break down even under small differences like the Simulation-to-Reality (Sim2Real) gap and require system identification and retraining for even minimal changes to the system. In this wor…

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
Zhihao Zhang, Chengyang Peng, Ekim Yurtsever, Keith A. Redmill
https://arxiv.org/abs/2509.04712

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
Automated vehicle control using reinforcement learning (RL) has attracted significant attention due to its potential to learn driving policies through environment interaction. However, RL agents often face training challenges in sample efficiency and effective exploration, making it difficult to discover an optimal driving strategy. To address these issues, we propose guiding the RL driving agent with a demonstration policy that need not be a highly optimized or expert-level controller. Specifi…

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Tobias Kern, Leon Tolksdorf, Christian Birkner
https://arxiv.org/abs/2507.11241

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-localization accuracy with visual and visual-inertial algorithms using cameras and an inertial measurement unit (IMU). For this purpose, ROS2-compatible visual and visual-inertial algorithms are selected, and datasets are chosen as a baseline for real-sized vehicles. A test drive is conducted to record data of reduced-…