考虑定位不确定性的无人驾驶安全规划方法  

Safety planning method for autonomous driving considering localization uncertainty

作　　者：单云霄 刘沅昊 Shan Yunxiao;Liu Yuanhao(School of Artificial Intelligence,Sun Yat-sen University,Zhuhai 519080,China;Shenzhen Research Institute,Sun Yat-sen University,Shenzhen 528406,China)

机构地区：[1]中山大学人工智能学院,珠海519080 [2]中山大学深圳研究院,深圳528406

出　　处：《中国图象图形学报》2024年第11期3280-3292,共13页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62232008);广东省青年基金项目(2020A1515110199);深圳市基础研究项目(JCYJ20210324122203009);CAST基金(SR-0030-2023-12-003);中国大学产业研究合作创新基金项目(2021ZYA03005)。

摘　　要：目的无人驾驶规划与控制是保障行驶安全的重要环节之一,现有的规划方法大多假定驾驶场景是精确感知的,忽略了行驶环境中存在的感知、定位等不确定性。忽略这些不确定性的因素将影响驾驶的安全。本文在考虑传感器数据不确定性的情况下,将系统中实际存在的定位不确定性融入规划系统,从而规划出更加安全的轨迹。方法通过研究基于栅格地图的不确定环境概率模型框架以及基于该表征框架的轨迹规划方法降低不确定性的影响,产生舒适安全的类人轨迹。该方法首先将先验地图转换为栅格地图作为全局栅格地图,接着结合定位系统将局部栅格地图初始化,然后在局部栅格地图中进行定位不确定性传播,最后在Frenet坐标系进行轨迹规划,使用局部栅格地图的占据概率计算候选轨迹代价,选择最优代价轨迹。结果本文方法在CARLA(CAR learning to act)仿真器中进行验证,通过仿真实验对比多种方法,验证了本文方法能够在定位不确定性环境下平稳行驶,安全地避开障碍物,在路径安全性和高效性上找到一个平衡点,在多种场景下本文考虑定位不确定性的方法通过率提高15%。结论本文提出了一种能够融入多种不确定性的环境表征框架,并将定位系统不确定性融入规划方法,实现了规划的安全性和效率的提升。Objective Autonomous driving planning methods currently assume the certainty of the information obtained.However,the actual situation contains a variety of uncertainties,and ignoring these uncertainties may lead to safety problems.The measurements provided by the sensors generally include the value of the state and the uncertainty of the state value,which is typically represented by the covariance matrix.At present,most planning methods do not utilize this uncertainty and choose to ignore it directly.These uncertainties will also have an impact on planning;when accumulated over time,the impact may even be sufficiently large to cause serious accidents.If a method or framework that can deal with this uncertainty is available,then additional reference information can be provided for the planning system,which will have a positive effect on driving safety.This paper aims to address a critical challenge in the field of autonomous driving:the effective consideration of uncertainty,particularly focusing on sensor measurement errors.In the complex and dynamic environment of autonomous driving,uncertainties can arise from various sources,posing substantial hurdles to accurate and safe planning.The study delves deep into understanding the intricacies of sensor measurement errors by concentrating on sensor uncertainty,which are vital components of perception systems in autonomous vehicles.The primary objective is to develop robust planning algorithms that can account for these uncertainties,ensuring that autonomous vehicles can make informed decisions even under imperfect or noisy sensor data.This research is pivotal for enhancing the reliability and safety of autonomous driving systems,ultimately paving the way for the widespread adoption of autonomous vehicles by addressing one of the key challenges in their deployment.Method The methodology in this article revolves around the innovative use of the grid map,a mathematical framework employed to characterize uncertainty as the occupancy probability within a grid map.The proc

关 键 词：不确定性 无人驾驶 栅格地图 轨迹规划 Frenet规划 传感器误差 

分 类 号：U463.6[机械工程—车辆工程] TP273[交通运输工程—载运工具运用工程]