2023年6月，UniAD（Unified Autonomous Driving）获CVPR2023最佳论文，UniAD将所有所有模块通过神经网络“...
nuPlan planning, Workshop on End-to-end Autonomous Driving, CVPR 2023 CARLA Autonomous Driving Challenge 2022, Machine Learning for Autonomous Driving, NeurIPS 2022 CARLA Autonomous Driving Challenge 2021, Machine Learning for Autonomous Driving, NeurIPS 2021 ...
[109] L. A. Rosero, I. P. Gomes, J. A. R. da Silva, T. C. dos Santos, A. T. M. Nakamura, J. Amaro, D. F. Wolf, and F. S. Osório, “A software architecture for autonomous vehicles: Team lrm-b entry in the first carla autonomous driving challenge,” 2020. [110] H....
With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, socially-aware robot assistant and public safety monitoring. Deciphering human behaviors to predict their...
I NTRODUCTIONSince 1988, the f irst autonomous vehicle, ALVINN, hasbeen recognised as a successful example of the use of neuralnetworks for autonomous driving [1]. Modern autonomousdriving technology was started since 2004, as DARPA [2]hosted its f irst grand challenge in self-driving, and ...
CARLA Autonomous Driving Challenge, Foundation Models for Autonomous Systems, CVPR 2024 nuPlan planning, Workshop on End-to-end Autonomous Driving, CVPR 2023 CARLA Autonomous Driving Challenge 2022, Machine Learning for Autonomous Driving, NeurIPS 2022 ...
2.1. End-to-End Autonomous Driving with RL 由于RL依赖试错,出于安全和数据效率的考虑,大部分应用于自主汽车的RL工作都是在仿真中进行的。其中使用最多的模拟器是TORCS [34],因为它是一个开源且简单易用的赛车游戏。研究者们用它来测试他们的新的actor-critic算法,以控制一辆汽车的离散动作(Mnih等[23])和连续...
However, achieving Level 4 and 5 autonomous driving remains a significant challenge for both academia and industry. Among the various modules of autonomous driving, High-Definition (HD) maps have become a crucial component due to their high precision ...
End-to-end autonomous driving decision-making is a popular research field in autonomous driving. In this paper, we propose an end-to-end decision-making model based on DDPG deep reinforcement learning. Firstly, we establish an end-to-end decision-making model to map driving state (such as ta...
Bench2Drive is a CARLA benchmark proposed by the paper Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving. It consists of 220 very short (~150m) routes split across all towns with 1 safety critical scenario in each route. Since it uses all towns ...