TrafficSignBench: Rule-Centric Closed-Loop Evaluation of Traffic-Sign Compliance in Autonomous Driving

arXiv:2609.38463 · cs.RO, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Benchmark — 30× in this paper · explained in 2 episodes
• Verifier — 7× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Intelligent Driver Model — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
• CaRL: Learning Scalable Planning Policies with Simple Rewards
• PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving
• BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
• Planning-oriented Autonomous Driving
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
• nuScenes: A multimodal dataset for autonomous driving
• CARLA: An Open Urban Driving Simulator
• LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
• The NVIDIA PilotNet Experiments
• Explicit error bounds for entanglement transformations between sparse multipartite states
• D-AWSIM: Distributed Autonomous Driving Simulator for Dynamic Map Generation Framework
• ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research
• DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
• Parting with Misconceptions about Learning-based Vehicle Motion Planning
• SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
• When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks
• PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving
• Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
• Enhancing SUMO simulator for simulation based testing and validation of autonomous vehicles

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• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving