6 papers · 1 filter
Driving risk emerges from the required two-dimensional joint evasive acceleration
Hao Cheng, Yanbo Jiang, Wenhao Yu +9
Most autonomous driving safety benchmarks use time-to-collision (TTC) to assess risk and guide safe behaviour. However, TTC-based methods treat risk as a one-dimensional closing pr…
ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning
Keyu Chen, Wenchao Sun, Hao Cheng +2
As the foundation of closed-loop training and evaluation in autonomous driving, traffic simulation still faces two fundamental challenges: covariate shift introduced by open-loop i…
ExpertAD: Enhancing Autonomous Driving Systems with Mixture of Experts
Haowen Jiang, Xinyu Huang, You Lu +6
Recent advancements in end-to-end autonomous driving systems (ADSs) underscore their potential for perception and planning capabilities. However, challenges remain. Complex driving…
Modified-Emergency Index (MEI): A Criticality Metric for Autonomous Driving in Lateral Conflict
Hao Cheng, Yanbo Jiang, Qingyuan Shi +5
Effective, reliable, and efficient evaluation of autonomous driving safety is essential to demonstrate its trustworthiness. Criticality metrics provide an objective means of assess…
RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation
Keyu Chen, Wenchao Sun, Hao Cheng +1
Achieving both realism and controllability in closed-loop traffic simulation remains a key challenge in autonomous driving. Dataset-based methods reproduce realistic trajectories b…
FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Keyu Chen, Yuheng Lei, Hao Cheng +3
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Exi…