在不稳定的通信环境中,针对连接和自动化车辆的安全和效率导向的自适应战略控制
Yangzhen Zhao1, Xuedong Hua1, Weijie Yu1
1School of Transportation, Southeast University, No.2 Southeast University Road, Nanjing 211189, PR China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, No.2 Southeast University Road, Nanjing 211189, PR China.
Accident; analysis and prevention
|July 1, 2025
概括
连接和自动化车辆 (CAV) 排队面临来自不稳定的通信环境 (UCE) 的挑战. 本研究介绍了适应性策略,以提高UCE下的安全性和效率,提高交通流的弹性.
科学领域:
- 运输工程 运输工程
- 控制系统 控制系统
- 网络通讯 网络通讯 网络通讯
背景情况:
- 连接和自动驾驶汽车 (CAV) 承诺提高交通安全和效率.
- 由于数据传输问题,不稳定的通信环境 (UCE) 会降低 CAV 排队的性能.
- 现有的研究不足地解决了UCE下的CAV排队策略.
研究的目的:
- 调查UCE对CAV排队性能的影响.
- 开发和评估适应性动态战略控制 (ADSC) 用于UCE下的CAV排队.
- 为不同的UCE场景确定最佳的ADSC配置和适用的策略.
主要方法:
- 模拟CAV排队使用修改的智能驾驶模型 (IDM).
- 使用通信干扰框架模拟UCE.
- 将动态通信拓集成到多类车辆控制中.
- 开发和测试5个自适应动态战略控制 (ADSC) 和3个混合开关ADSC.
主要成果:
- 连续UCE (CUCE) 的安全风险比间歇UCE (IUCE) 的安全风险更大.
- 结合IUCE场景被发现是对排的表现最有害的.
- 积极的加速行为显著增加了碰撞风险和速度振荡.
- 拟议的混合切换ADSCs在CAV中队的性能上取得了显著的改善.
结论:
- UCE对CAV排队安全性和效率构成重大挑战.
- 适应式和混合切换ADSC提供了弹性控制策略,以减轻UCE的影响.
- 该研究为在不同的UCE条件下实施有效的ADSC提供了建议,以确保流的交通流动.
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