2 citations · 3 across the 12 of their papers we have counts for
14 papers
Plans Are Not Executions: Adapting Frozen World-Model Planners to Controller-Induced Execution Shift
Danqi Zhao, Keyu Chen, Wenchao Sun +6
World-model planners evaluate candidate trajectories by predicting their future consequences, implicitly relying on the plan-to-motion relationship learned during training. At depl…
EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles
Yingkai Yang, Ashton Yu Xuan Tan, Bowen Li +10
Reliable risk assessment remains a central challenge for Autonomous Vehicles (AVs). Despite advances in automation, passenger cognition provides a non-intrusive auxiliary signal th…
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
Ziang Guo, Chen Min, Xuefeng Zhang +5
End-to-end (E2E) autonomous driving aims to directly map sensory observations to driving actions, but its real-world deployment is hindered by evolving data distributions and the h…
CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies
Keyu Chen, Nanfei Ye, Yida Wang +4
Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induced distribution shift. Existi…
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…
SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
Wenchao Sun, Xuewu Lin, Keyu Chen +4
End-to-end multi-modal planning has been widely adopted to model the uncertainty of driving behavior, typically by scoring candidate trajectories and selecting the optimal one. Exi…
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…
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…
DriveCamSim: Generalizable Camera Simulation via Explicit Camera Modeling for Autonomous Driving
Wenchao Sun, Xuewu Lin, Keyu Chen +4
Camera sensor simulation serves as a critical role for autonomous driving (AD), e.g. evaluating vision-based AD algorithms. While existing approaches have leveraged generative mode…
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…
Passenger hazard perception based on EEG signals for highly automated driving vehicles
Ashton Yu Xuan Tan, Yingkai Yang, Xiaofei Zhang +9
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception a…
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…
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
Wenchao Sun, Xuewu Lin, Yining Shi +3
The well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and e…
Neural Radiance Field in Autonomous Driving: A Survey
Lei He, Leheng Li, Wenchao Sun +5
Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel…