Tootfinder

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
Xiaoyun Qiu, Haichao Liu, Yue Pan, Jun Ma, Xinhu Zheng
https://arxiv.org/abs/2509.22550

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment
In mixed-traffic environments, where autonomous vehicles (AVs) interact with diverse human-driven vehicles (HVs), unpredictable intentions and heterogeneous behaviors make safe and efficient lane change maneuvers highly challenging. Existing methods often oversimplify these interactions by assuming uniform patterns. We propose an intention-driven lane change framework that integrates driving-style recognition, cooperation-aware decision-making, and coordinated motion planning. A deep learning c…

My latest haul, for anyone interested, contains the following volumes:
Test Driven Development by Example: https://www.powells.com/book/test-driven-development-by-example-9780321146533
Building Serverless Applications on Knative:

FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Yuzhen Long, Songze Li
https://arxiv.org/abs/2509.24408 https://arx…

FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models (LLMs), where specialized agents collaborate to perceive, reason, and plan. A key component of these systems is the shared function library, a collection of software tools that agents use to process sensor data and navigate complex driving environments. Despite its critical role in agent decision-making, the function library remains an under-explored vulnerability. In this paper, we introd…

A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems
Stefano Carlo Lambertenghi, Mirena Flores Valdez, Andrea Stocco
https://arxiv.org/abs/2509.22379 htt…

A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems
Simulation-based testing is a cornerstone of Autonomous Driving System (ADS) development, offering safe and scalable evaluation across diverse driving scenarios. However, discrepancies between simulated and real-world behavior, known as the reality gap, challenge the transferability of test results to deployed systems. In this paper, we present a comprehensive empirical study comparing four representative testing modalities: Software-in-the-Loop (SiL), Vehicle-in-the-Loop (ViL), Mixed-Reality (…

Data-Driven Resilience Assessment against Sparse Sensor Attacks
Takumi Shinohara, Karl Henrik Johansson, Henrik Sandberg
https://arxiv.org/abs/2509.25064 https://

Data-Driven Resilience Assessment against Sparse Sensor Attacks
We present a data-driven framework for assessing the attack resilience of linear time-invariant systems against malicious false data injection sensor attacks. Based on the concept of sparse observability, data-driven resilience metrics are proposed. First, we derive a data-driven necessary and sufficient condition for assessing the system's resilience against sensor attacks, using data collected without any attacks. If we obtain attack-free data that satisfy a specific rank condition, we can ex…

Malaysia's AI-Driven Education Landscape: Policies, Applications, and Comparative Insights for a Digital Future
Fadhilah Jamaluddin, Ahmad Hakiim Jamaluddin, Faridzah Jamaluddin, Faathirah Jamaluddin
https://arxiv.org/abs/2509.21858

Malaysia's AI-Driven Education Landscape: Policies, Applications, and Comparative Insights for a Digital Future
Artificial Intelligence (AI) is transforming education globally, and Malaysia is leveraging this potential through strategic policies to enhance learning and prepare students for a digital future. This article explores Malaysia's AI-driven education landscape, emphasising the National Artificial Intelligence Roadmap 2021-2025 and the Digital Education Policy. Employing a policy-driven analysis, it maps AI applications in pedagogy, curriculum design, administration, and teacher training across p…

🇺🇦 Auf radioeins läuft...
It's Immaterial:
🎵 Driving Away From Home
#NowPlaying #ItsImmaterial
https://dominicdawson.bandcamp.com/album/driving-away-from-home-dominic-dawson-re-versions
https://open.spotify.com/track/2oDxd2tiYYMddJa1o1Zn3L

Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Shiyi Liang, Xinyuan Chang, Changjie Wu, Huiyuan Yan, Yifan Bai, Xinran Liu, Hang Zhang, Yujian Yuan, Shuang Zeng, Mu Xu, Xing Wei
https://arxiv.org/abs/2509.22756

Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive c…

Data-Driven Optimal Power Flow: A Behavioral Systems Approach
Sebastian Otzen, Hannes M. H. Wolf, Christian A. Hans
https://arxiv.org/abs/2509.25120 https://

Data-Driven Optimal Power Flow: A Behavioral Systems Approach
The increasing decentralization of power systems driven by a large number of renewable energy sources poses challenges in power flow optimization. Partially unknown power line properties can render model-based approaches unsuitable. With increasing deployment of sensors, data-driven methods rise as a promising alternative. They offer the flexibility to adapt to varying grid structures and unknown line properties. In this paper, we propose a novel data-driven representation of nonlinear power fl…

Collusion-Driven Impersonation Attack on Channel-Resistant RF Fingerprinting
Zhou Xu, Guyue Li, Zhe Peng, Aiqun Hu
https://arxiv.org/abs/2509.22154 https://

Collusion-Driven Impersonation Attack on Channel-Resistant RF Fingerprinting
Radio frequency fingerprint (RFF) is a promising device identification technology, with recent research shifting from robustness to security due to growing concerns over vulnerabilities. To date, while the security of RFF against basic spoofing such as MAC address tampering has been validated, its resilience to advanced mimicry remains unknown. To address this gap, we propose a collusion-driven impersonation attack that achieves RF-level mimicry, successfully breaking RFF identification systems…