arXiv AI
Jun 16
Explainable deep learning improves human mental models of self-driving cars
arXiv:2411. 18714v3 Announce Type: replace-cross Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving.
System helps humans predict when self-driving cars will make mistakes
A new method named CW‑Net converts the reasoning of an autonomous vehicle’s AI into understandable concepts, allowing humans to see why the car behaves a certain way. By translating the AI’s internal logic into clear explanations, the system helps users predict when a self‑driving car might make mistakes. This approach bridges the gap between complex machine learning processes and human comprehension.
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arXiv Computation and Language
Sep 3
Beyond Textual Chain-of-Thought: A Survey on Action-Grounded Reasoning in Autonomous Driving
The paper surveys the transition from textual chain-of-thought reasoning to action-grounded reasoning in autonomous driving, highlighting that driving decisions require continuous actions that mirror the spatiotemporal structure of the physical world. It reviews 171 papers, categorizing 130 methods into four main types—language-based, visual-spatial, latent-dynamic, and externalized reasoning—along with 13 subtypes linked to specific regions of interest. The authors argue that the future of driving agent reasoning lies in intermediate representations that are grounded in reality, linked to real-time actions, and verifiable within safety-critical systems.
arXiv AI
Jul 8
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
arXiv:2607. 06328v1 Announce Type: new Abstract: The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior.
arXiv Computer Vision
Aug 31
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset
The paper introduces the KITScenes LongTail dataset, a curated collection of rare driving scenarios designed to evaluate how well reasoning models in autonomous driving follow their own reasoning. The authors find that many current models frequently diverge between the actions they state in their reasoning chains and the actions they actually execute, a phenomenon they term incoherence. They show that when the reasoning and execution disagree, the reasoning is often correct, and enforcing coherence via a kinematic model can improve motion planning, indicating that coherent action based on stated reasoning is essential for trustworthy autonomous driving.
arXiv Computer Vision
Oct 2
End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
arXiv:2610.01746v1 Announce Type: cross Abstract: Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures off...
Hugging Face Trending Papers
Sep 7
A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction
This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either detecting objects independently or leveraging visu...
arXiv AI
Aug 13
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
arXiv:2608. 12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments.
arXiv AI
Sep 4
LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving
LightEMMA is a longitudinal evaluation framework that tests the autonomous driving performance of vision‑language models (VLMs) without fine‑tuning or prompt engineering. Using this protocol, the authors evaluated 15 VLMs from five major families on the nuScenes prediction benchmark and found that larger, more capable models do not consistently outperform earlier generations. The study identifies common failure modes such as overreliance on historical actions and difficulty reconciling conflicting visual cues, underscoring the need for domain‑specific adaptation to enhance VLM safety in autonomous driving.
arXiv Computer Vision
Sep 30
CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving
CAR‑VLA is a Vision‑Language‑Action model for autonomous driving that jointly considers scene complexity and dynamic risk to determine reasoning depth, urgency, and focus. It maps four complexity‑risk categories to three reasoning modes—Fast Intuition, Slow Thinking, and Reflex Response—each tailored to different driving scenarios. The model is trained via progressive supervised learning and reinforcement learning, achieving competitive performance on NAVSIM and Navhard benchmarks and demonstrating risk‑aware reasoning in high‑risk scenarios.
arXiv Machine Learning
Jun 15
RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation
arXiv:2606. 14010v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction.
arXiv AI
Jul 1
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
arXiv:2606. 31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawed reasoning and unsafe heuristics.
arXiv AI
Jul 28
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.