基于自动驾驶汽车的多任务融合战略决策和规划方法
Weiguo Liu1,2, Zhiyu Xiang1, Han Fang3
1Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种多功能模拟平台,以及一种用于自动驾驶的深度强化学习 (DRL) 的多任务融合策略. 该方法增强了在各种条件下代理的概括性和性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 深度强化学习 (DRL) 是自动驾驶的前沿领域,使代理商能够学习决策策略.
- 开发和测试真正的自动驾驶汽车是昂贵的,耗时的,有风险的,需要高效的模拟平台.
- 稀缺的奖励信号和对未见的场景的不良概括是自动驾驶DRL的重大挑战.
研究的目的:
- 开发一个联合模拟开发和验证平台,以加快对自动驾驶DRL算法的测试和代.
- 提出一种使用多任务融合的新型深度决定性政策梯度 (DDPG) 方法,以提高DRL代理人的概括能力.
- 为了应对稀缺奖励的挑战,并在各种环境条件下提高代理的性能.
主要方法:
- 使用VTD-CarSim和Tensorflow深度学习框架设计和实施了一个联合的模拟平台.
- 提出了一种多任务融合策略,将DRL决策策划与图像语义细分结合起来.
- 采用了深度决定性政策梯度 (DDPG) 算法,在主要和辅助任务之间共享网络部分,以减少过度装配.
主要成果:
- 开发的模拟平台表现出高度的多功能性,允许轻松替换模块以进行定制算法验证.
- 多任务融合DRL策略显示出竞争性性能,在特定任务中表现优于其他DRL算法.
- 提出的方法显著提高了汽车决策规划算法的概括能力.
结论:
- 联合模拟平台有效地加速了自动驾驶DRL算法的开发和验证.
- 多任务融合DRL策略提高了代理性能和通用性,使自动驾驶系统更加稳健.
- 这项研究有助于克服自动驾驶DRL的关键挑战,为更安全,更高效的系统铺平道路.
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