（一万小时计划）十二月十七日总结
十二月十七日
navida版本查看命令：navidia-smi 查看cuda版本号
Machine Learning for Automated Drivinghttps://link.springer.com/article/10.1007/s38311-019-0150-z
Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles：https://www.sciencedirect.com/science/article/pii/S0888327017303825
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving：https://arxiv.org/abs/1808.05819
Improved Multi-Agent Deep Deterministic Policy Gradient for Path Planning-Based Crowd Simulation：https://ieeexplore.ieee.org/abstract/document/8865095
Autonomous Highway Driving using Deep Reinforcement Learning：https://ieeexplore.ieee.org/abstract/document/8914621?casa_token=XKPE5mq7H6oAAAAA:lXUP61FrnUlHjAMb_-SqM5V968NkGU4Fa5z5_M7sLiPMdL3q746R9SaDC3_oI4BzgC66WpMETg
Motion Planning Networks：https://ieeexplore.ieee.org/abstract/document/8793889?casa_token=qhrKwXzPVb4AAAAA:vsGka0Ci8NUHT5RmBu-ZgD7YkqYp3-pIWLNvKf1AuTxkk_UCGdEZOLi8PWJXqyK3xVuRBOxOwQ
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning：https://www.sciencedirect.com/science/article/pii/S0921889018302021
Self-driving cars: A survey：https://www.sciencedirect.com/science/article/pii/S095741742030628X
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles：https://arxiv.org/abs/1910.04586
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving：https://paperswithcode.com/paper/integrating-deep-reinforcement-learning-with
无人车的决策和规划分别是负责什么的？两者的差别和联系是什么：https://www.zhihu.com/question/41013884
A Survey of Motion Planning and ControlTechniques for Self-driving Urban Vehicles
Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review：https://ieeexplore.ieee.org/abstract/document/9158529?casa_token=NDPsq5uKpS0AAAAA:koNyK0AwrJcnBbt0MGk2ZcFQA_nJlZsC3VV-o-VPEtIb5OEeZKgHUofoJ_4mdzh6IdzFqi4O6w
Simulation-Based Reinforcement Learning for Real-World Autonomous Driving：https://ieeexplore.ieee.org/abstract/document/9196730?casa_token=Vk5TGsBnYVYAAAAA:QeEI6A_8z69cYjJUN1zNX-0efWAGHiUA3xwcsTKv46hVfb6uEfnNoPgVeU7AKKVdY6YWs_E1iA
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints
A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning：https://paperswithcode.com/paper/a-hierarchical-architecture-for-sequential
视频：https://www.youtube.com/watch?v=0fLSf3NO0-s&ab_channel=LexFridman&t=6s mit motion planning
https://www.youtube.com/watch?v=QbbOxrR0zdA&ab_channel=PyData&t=3s
openai env：https://github.com/eleurent/highway-env
