摘要
决策与规划是自动驾驶系统的中枢,是提高自动驾驶车辆行驶安全、驾乘体验、出行效率的关键.其面临的主要挑战在于如何满足自动驾驶所需的极高可靠性和安全性,以及如何有效应对场景复杂性、环境多变性、交通动态性、博弈交互性及信息完备性并产生类人化的驾驶行为,使车辆自然地融入交通生态.为全面了解决策与规划的前沿问题与研究进展,对其技术要点进行系统梳理与总体概述.首先,从数据驱动的驾驶行为预测、概率模型的驾驶行为预测、个性化驾驶行为预测三方面综述了面向态势认知的行为预测的研究进展;其次,将行为决策总结归纳为反应式决策、学习式决策、交互式决策并逐一进行了分析;再次,从方法论的角度对运动规划及其应用进行对比分析,具体包括图搜索方法、采样方法、数值方法、拟合插值曲线方法等;然后,针对端到端的决策规划的关键科学问题和主要研究进展进行了归纳分析;最后,总结了决策规划对提升自动驾驶车辆智能化水平的重要影响,并展望了其未来的发展趋势与面临的技术挑战.
Abstract
Decision-making and planning are the core functions of automated driving systems and the key to improving the driving safety,driving experience and travel efficiency of automated vehicles.The main challenges faced by decision-making and planning are how to meet the extremely high reliability and safety requirements for automated driving,and how to effectively deal with scenario complexity,environmental variability,traffic dynamicity,game interactivity,and information completeness,as well as how to generate human-like driving behavior,so that vehicles can integrate into the traffic ecosystem naturally.A systematic and overall review of the technical points of decision-making and planning is presented in this paper to gain a comprehensive understanding of their frontier issues and research progress.Firstly,the research progress of situational awareness-oriented behavior prediction is reviewed from three perspectives,namely data-driven driving behavior prediction,probabilistic model driving behavior prediction,and personalized driving behavior prediction.Secondly,behavior decision-making is summarized into reactive decision-making,learning decision-making and interactive decision-making,all of which are analyzed in turn.Thirdly,motion planning and its applications are compared and analyzed from a methodological perspective,including graph search methods,sampling methods,numerical methods,interpolation and curve fitting methods,etc.Additionally,the key scientific issues and major research progress of end-to-end decision-making and planning are summarized and analyzed.Finally,the significant impact of decision-making and planning on improving the intelligent level of automated vehicles is summarized,and the future development trends and technical challenges are prospected.
基金项目
国家自然科学基金项目(52172386)
国家自然科学基金项目(U22A20247)
国家自然科学基金项目(52302494)
吉林省长春市重大科技专项(20220301009GX)
NETL
NSTL
维普
万方数据