连接和自动驾驶汽车的基于学习的决策技术:框架,审查和未来趋势
Qi Liu1, Xueyuan Li1, Yujie Tang2
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
图形增强学习 (GRL) 增强了混合交通中连接和自动驾驶汽车 (CAV) 的决策. 本综述探讨了GRL方法,以实现更安全,更高效的自动驾驶系统.
科学领域:
- 智能运输系统 智能运输系统
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 互联和自动驾驶汽车 (CAV) 对未来的交通至关重要,但混合自动驾驶交通 (CAV和人类驾驶汽车) 却带来了挑战.
- 在过渡到完全自治期间,CAV的有效决策对于安全和效率至关重要.
- 深度增强学习 (DRL) 已经显示出希望,但图形增强学习 (GRL) 为建模复杂的车辆交互提供了卓越的能力.
研究的目的:
- 为CAV决策提供基于GRL的方法提供全面的审查.
- 为了解自动驾驶决策技术建立一个通用的GRL框架.
- 确定自动驾驶的GRL中的挑战和未来的研究方向.
主要方法:
- 从混合自主交通建设的角度审查GRL方法.
- 检查动态驾驶环境的图形表示技术.
- 总结有关图形神经网络 (GNN) 和自动驾驶中的DRL的相关工作.
- 编制用于评估决策绩效的验证方法.
主要成果:
- 通过准确地表示车辆间影响和动态交通,GRL方法显示了改善CAV决策的巨大潜力.
- 该审查基于流量组成,环境表示和底层网络架构对GRL方法进行了分类.
- 确定了基于GRL的自动驾驶系统的关键验证策略.
结论:
- 在混合交通环境中,GRL是促进CAV决策的有希望的方法.
- 需要进一步的研究来应对这些挑战,并充分利用GRL在自动驾驶方面的潜力.
- 本综述是研究人员开发基于GRL的自动驾驶汽车解决方案的基础资源.
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