38 citations · 69 across the 28 of their papers we have counts for
10 papers · 1 filter
ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models
Qichao Zhang, Xing Fang, Jiaqi Fang +4
Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable,…
Learning from Mistakes: Post-Training for Driving VLA with Takeover Data
Yinfeng Gao, Deqing Liu, Qichao Zhang +7
Current Vision-Language-Action (VLA) paradigms in end-to-end autonomous driving rely on offline training from static datasets, leaving them vulnerable to distribution shift. Recent…
PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
Yinfeng Gao, Qichao Zhang, Deqing Liu +8
End-to-end autonomous driving policies based on Imitation Learning (IL) often struggle in closed-loop execution due to the misalignment between inadequate open-loop training object…
Dream to Drive with Predictive Individual World Model
Yinfeng Gao, Qichao Zhang, Da-wei Ding +1
It is still a challenging topic to make reactive driving behaviors in complex urban environments as road users' intentions are unknown. Model-based reinforcement learning (MBRL) of…
Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving
Ding Li, Qichao Zhang, Zhongpu Xia +4
Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal couple…
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Ding Li, Qichao Zhang, Shuai Lu +2
This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-…
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving
Qichao Zhang, Yinfeng Gao, Yikang Zhang +5
Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying…
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
Yuqi Liu, Qichao Zhang, Dongbin Zhao
Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. Fo…
Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment
Haoran Li, Qichao Zhang, Dongbin Zhao
This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to…
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints
Junjie Wang, Qichao Zhang, Dongbin Zhao +1
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network…