A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI:10.1109/ICCVE45908.2019.8965238

Johannes Betz, A. Wischnewski, Alexander Heilmeier, Felix Nobis, Leonhard Hermansdorfer, Tim Stahl, T. Herrmann, M. Lienkamp

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引用次数: 25

Abstract

Based on a software architecture for autonomous driving presented and tested in an autonomous level-5 race-car in 2018 this paper describes in detail the evolutionary enhancement of this software architecture. The architecture combines the autonomous software layers perception, planning and control, which were modularized in the core software. The focus of this paper is the detailed description of how we enhanced the software with a module for an object list creation, a module for the behavioral planning and a module for the creation of dynamic trajectories. These enhancements allow the car to overtake other cars and static obstacles autonomously when driving on the race track. Furthermore, we present with a high novelty value the software module for a vehicle performance maximization, which consists of a control performance assessment and a friction estimation. The software architecture displayed in this paper will be tested and evaluated in the Roborace Season Alpha in 2019.

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极限操纵下自主赛车动态路径规划的软件体系结构

基于2018年在自动驾驶5级赛车上提出和测试的自动驾驶软件架构，本文详细描述了该软件架构的进化增强。该体系结构将感知、规划和控制三个自治软件层组合在一起，在核心软件中进行模块化。本文的重点是详细描述了我们如何通过对象列表创建模块、行为规划模块和动态轨迹创建模块对软件进行增强。这些增强功能使汽车在赛道上行驶时能够自动超越其他车辆和静态障碍物。此外，我们提出了具有较高新颖性的车辆性能最大化软件模块，该模块由控制性能评估和摩擦估计组成。本文展示的软件架构将在2019年的roboace Alpha赛季中进行测试和评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)

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