车路云一体化架构下面向舒适性提升的自动驾驶速度智能决策方法  

Intelligent Speed Planning Approach for Comfort Improvement of Autonomous Vehicles Based on Vehicle-road- cloud Integration Architecture

作　　者：陈菁 赵聪 马裕城 刘东杰 暨育雄[1,2] 杜豫川[1,2] CHEN Jing;ZHAO Cong;MA Yu-cheng;LIU Dong-jie;JI Yu-xiong;DU Yu-chuan(Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China;College of Transportation Engineering,Tongji University,Shanghai 201804,China;School of Transportation,Southeast University,Nanjing 210096,Jiangsu,China)

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804 [2]同济大学交通运输工程学院,上海201804 [3]东南大学交通学院,江苏南京210096

出　　处：《中国公路学报》2025年第2期243-257,共15页China Journal of Highway and Transport

基　　金：国家重点研发计划项目(2022YFF0604900);上海市教育委员会科研创新计划项目(2021-01-07-00-07-E00092)。

摘　　要：舒适性是影响自动驾驶技术接受度和信任度的关键因素,也是实现高质量自动驾驶服务的基础。如何在安全、高效行驶的前提下提升舒适性是自动驾驶应用面临的重要挑战。然而,交通条件、路面质量、车辆加减速均影响乘客舒适感受,使得复杂环境下舒适性致因难以判断,导致自动驾驶速度决策存在偏差,进而影响自动驾驶车辆控制效果。因此,理解路面质量、道路交通、速度决策与舒适感受之间的因果关系,对于自动驾驶舒适性提升至关重要。据此,在车路云一体化架构下提出面向舒适性提升的自动驾驶智能决策控制框架。该框架将车载单元和边缘云视为智能体,建立基于深度强化学习的自动驾驶速度智能决策模型,利用反事实推理和专家推荐实现训练样本双向增广,以提升智能体对驾驶环境和任务的理解能力。在算例中,结合上海市路面高程数据和NGSIM数据集建立仿真环境,在不同路面和交通条件下对速度决策模型进行训练和测试。研究结果表明:反事实推理模型可在训练样本充足情况下,解析路面和交通状态、速度决策、决策奖励之间的因果关系,剖析状态的重要和次要部分,明确不同阶段速度决策的关注重点。相比于传统强化学习模型,采用双向增广策略的速度智能决策模型可在安全和高效行驶的前提下,分别降低25.71%、18.89%的纵向加速度变化率、烦恼率,自动驾驶舒适性提升明显,速度决策结果具备可解释性。所提出的自动驾驶决策控制框架和速度智能决策方法可支持自动驾驶舒适性在线提升,推动自动驾驶出行服务发展。Ride comfort is a crucial factor influencing the acceptance and trustworthiness of autonomous driving technology,which is the foundation of high-quality autonomous driving services.How to continuously improve ride comfort while ensuring safety and efficiency is a key challenge for the application of autonomous vehicles.However,traffic conditions,pavement quality,and acceleration and deceleration of vehicles have an impact on passenger sensation,leading to difficulties in discerning the factors contributing to passenger comfort in complex traffic environments.This further results in speed planning biases and affects vehicle control effectiveness.Therefore,understanding the causal relationship between pavement quality,traffic conditions,speed decisions,and comfort sensation is crucial for enhancing the ride comfort of autonomous driving.In this regard,based on the vehicle-road-cloud integration architecture,this study proposes an intelligent decision-making and control framework for ride comfort improvements in autonomous driving.First,we consider onboard units and edge clouds as intelligent agents.Then,an intelligent speed planning model is constructed for autonomous driving based on deep reinforcement learning,utilizing counterfactual reasoning and expert recommendations to increase training samples in two directions and enhance the understanding of the driving environment and tasks.In experiments,a simulation environment was established using pavement data from Shanghai and traffic data from Next Generation Simulation(NGSIM).Speed control models were trained and tested under different pavement and traffic conditions.The experimental results show that with sufficient training samples,the counterfactual reasoning model can analyze the causal relationships between pavement and traffic conditions,speed planning decisions,and decision rewards.The counterfactual reasoning model analyzes the importance of states and clarifies the focusing points of speed planning at different stages.On the premise of driving safety and

关 键 词：交通工程 自动驾驶 深度强化学习 速度决策 反事实推理 舒适性 

分 类 号：U495[交通运输工程—交通运输规划与管理]