6 papers
Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving
Cheng Gong, Haoyang Wang, Chao Lu +2
Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations. However, most learning-based…
Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities
Zirui Li, Yunlong Lin, Guodong Du +5
Artificial intelligence underpins most smart city services, yet deep neural network (DNN) that forecasts vehicle motion still struggle with catastrophic forgetting, the loss of ear…
Escaping Stability-Plasticity Dilemma in Online Continual Learning for Motion Forecasting via Synergetic Memory Rehearsal
Yunlong Lin, Chao Lu, Tongshuai Wu +5
Deep neural networks (DNN) have achieved remarkable success in motion forecasting. However, most DNN-based methods suffer from catastrophic forgetting and fail to maintain their pe…
H2C: Hippocampal Circuit-inspired Continual Learning for Lifelong Trajectory Prediction in Autonomous Driving
Yunlong Lin, Zirui Li, Guodong Du +5
Deep learning (DL) has shown state-of-the-art performance in trajectory prediction, which is critical to safe navigation in autonomous driving (AD). However, most DL-based methods…
Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
Yuchun Wang, Cheng Gong, Jianwei Gong +1
Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires conside…
Beyond Imitation: A Life-long Policy Learning Framework for Path Tracking Control of Autonomous Driving
C. Gong, C. Lu, Z. Li +3
Model-free learning-based control methods have recently shown significant advantages over traditional control methods in avoiding complex vehicle characteristic estimation and para…