基于自适应抽样的自动驾驶汽车的智能控制监督的关键参与者和人类驾驶经验
Jin Zhang1, Nan Ma2, Zhixuan Wu3
1Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
这项研究引入了一种新的自动驾驶方法,将人类经验与深度强化学习 (DRL) 结合起来,以实现高效的决策. 这种方法提高了学习速度和在复杂的交通场景中的性能.
科学领域:
- 自动驾驶系统 自动驾驶系统
- 智能运输系统 智能运输系统
- 机器学习用于控制控制.
背景情况:
- 由于环境复杂性和参与者行为,在密集的交通中自动驾驶具有挑战性.
- 传统的基于规则的方法在各种驾驶场景中难以适应.
- 深度强化学习 (DRL) 是有前途的,但由于探索效率低下和学习速度缓慢而受到影响.
研究的目的:
- 为自动驾驶汽车开发一种智能控制方法,克服传统DRL的局限性.
- 提高自动驾驶代理人的学习效率和政策优化.
- 利用人类驾驶经验,在复杂的交通中改善决策.
主要方法:
- 这是一种混合方法,将监督学习与DRL结合起来,并结合了人类驾驶数据.
- 一种适应平衡采样方法,以提高勘探效率.
- 详细的奖励功能设计,重点关注交通效率和其他关键指标.
主要成果:
- 拟议的方法可以在复杂的交通环境中有效控制车辆.
- 实验结果表明,与现有的DRL方法相比,其性能优越.
- 人类经验和适应性抽样的整合显著提高了学习效率.
结论:
- 由人类经验指导的联合监督学习和DRL方法为自动驾驶提供了更高效和更有效的解决方案.
- 这种方法显示出在自动驾驶汽车中推进智能决策的巨大潜力.
- 未来的工作可以探索适应性抽样和奖励函数设计的进一步改进.
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