Showing 1–20 of 23 results
/ Date/ Name
Apr 11, 2026AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language ModelsOct 3, 2024From Imitation to Exploration: End-to-end Autonomous Driving based on World ModelAug 19, 2021Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior RegularizationNov 18, 2023Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingMar 18, 2020Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous VehiclesApr 18, 2021Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement LearningNov 18, 2023Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous DrivingMay 31, 2024HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking ScenariosApr 17, 2024Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based LocalizationAug 15, 2024CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object DetectionOct 15, 2025A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space ParkingMar 17, 2025Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive SurveyOct 9, 2025ASBench: Image Anomalies Synthesis Benchmark for Anomaly DetectionOct 7, 2024Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal LawDec 2, 2020Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement LearningOct 14, 2022A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback LoopsNov 16, 2023Interpretable Reinforcement Learning for Robotics and Continuous ControlOct 16, 2023Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable AlgorithmsNov 11, 2023Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP ImaginationAug 1, 2018Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior