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对于微观交通模拟测试评估的CAV,一个全面的生态驾驶战略

Ozgenur Kavas-Torris¹, Levent Guvenc¹

¹Automated Driving Lab, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210, USA.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

连接和自动驾驶汽车 (CAV) 的新生态驾驶战略通过管理多种驾驶模式来优化燃油经济性. 这个系统在没有碰撞的模拟中节省了6.41%的燃料.

科学领域:

* 智能交通运输系统
* 汽车工程汽车工程
* 可持续的流动性

背景情况:

* 互联和自动驾驶汽车 (CAV) 的日益普及需要高效的能源管理策略.
*现有的生态驾驶方法往往缺乏全面整合多种驾驶模式和先进的通信.

研究的目的:

* 为CAVs提出一个全面的决定性生态驾驶战略.
*通过同时优化各种驾驶模式的速度配置,提高燃油经济性.
* 通过使用高级控制器,确保在模式之间进行安全和平稳的过渡.

主要方法:

* 开发用于CAV的确定性生态驾驶控制器,包括车辆与基础设施 (V2I) 和车辆与车辆 (V2V) 通信.
* 实现多种驾驶模式,同时根据个人约束计算速度.
* 使用高级 (HL) 控制器进行无模式转换.
*通过微观交通模拟进行验证,以量化燃油经济性改进.

主要成果:

*与基线驾驶模式相比,HL控制器显示了显著的燃油经济性改善.
* 该策略确保了ego CAV与其他交通车辆之间的无碰撞运行.
* 微观交通模拟显示,在拟议的生态驾驶战略下,CAV的燃油经济性得到了6.41%的改善.
* 该系统有效地适应了CAV在不断变化的环境和交通限制下驾驶模式.

结论:

* 提出的综合决定性生态驾驶战略有效地提高了CAV的燃油经济性.
* V2I和V2V通信的整合,加上智能模式管理,对于高效的自动驾驶至关重要.
* 该战略通过减少自动驾驶汽车的燃料消耗,为可持续运输提供了可行的解决方案.

关键词:

动态编程是动态的编程.生态驾驶生态驾驶生态合作适应式巡航控制生态合作交通模拟的交通模拟.速度轨迹的速度轨迹.

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