In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative
adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and …

Self-reflecting about our performance (eg, how confident we are) before doing a task is
essential for decision making, such as selecting the most suitable tool or choosing the best …

Soft actor critic (SAC) is an off policy deep reinfor-cment learning algorithm that learns by
using the principle of maximum entropy regularization. It updates the policy stochas-tically …

Unpredictable situations frequently occur in real driving environments, and it is often difficult
to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to …

The PID (Proportional-Integral-Derivative) controller has been broadly applied in many
control engineering tasks due to its simplicity and fast computation as the model-free low …

In recent years, deep reinforcement learning has attracted significant attention from both
industry and academia and is used in solving various complex problems. In this paper, an …

确保行人的安全对自动驾驶汽车而言至关重要, 同时也具有一定挑战. 经典的行人避撞策略无法
应对不确定性, 而基于学习的方法缺乏明确的性能保障. 本文提出一种基于混合强化学习的行人 …

Multi-agent path planning is a complex problem aiming to find the shortest trajectory. In this
paper, we propose a cooperative path planning based on a hybridisation of two motion …

The contradiction between increasing traffic and the relatively poor roundabout infrastructure
is getting stronger. The control and optimization of the macroscopic traffic flow needs to be …

Autonomous vehicles will optimize our transportation infrastructure and could in the long run
improve our lifestyles. Machine Learning has recently advanced, allowing us to get closer to …