Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using state-of-the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. We perform experiments to validate our framework and we conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian's crossing.
翻译:在行人在场的情况下,运动控制算法对于发展安全可靠的自主车辆(AVs)至关重要。传统的运动控制算法依赖于人工设计的决策政策,而这种政策忽视了AVs和行人之间的相互作用。另一方面,在深强化学习方面最近的进展使得可以自动学习没有人工设计的政策。为了解决行人在场情况下的决策问题,作者采用了一个基于社会价值导向和深强化学习的框架,能够产生具有不同驾驶风格的决策政策。该政策在模拟环境中使用最先进的DRL算法进行了培训。还采用了一种适用于DRL培训的新的计算效率高的行人模型。我们进行了实验,以验证我们的框架,并用两种不同的无模型的深强化学习算法对获得的政策进行了比较分析。模拟结果显示,开发的模型如何展示自然驾驶行为,例如短途停留,以便利行人过境。