集成模块化管道与端到端的学习:一个混合方法,坚固可靠的自动驾驶系统
Luis Alberto Rosero1, Iago Pachêco Gomes1, Júnior Anderson Rodrigues da Silva1
1Institute of Mathematics and Computer Science, University of São Paulo, Ave. Trabalhador São-Carlense, 400, São Carlos 13564-002, SP, Brazil.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究介绍了一种新的混合动力自动驾驶系统,该系统将模块化感知和控制与数据驱动的路径规划相结合. 这种创新方法在基于地图和无地图的场景中增强了导航能力,在重大竞争中获得了最高排名.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自动驾驶系统利用各种各样的导航策略,包括模块化和端到端的方法,每个都有特定的优缺点.
- 现有的系统通常专门用于基于地图或无地图的导航,限制了它们的适应性.
- 需要集成系统,利用模块化和端到端学习的好处,以实现强大的自主导航.
研究的目的:
- 为自动驾驶导航提出和评估一种新的混合架构.
- 整合模块化感知和控制与数据驱动路径规划,以提高性能.
- 在基于地图和无地图的驾驶场景中证明系统的有效性.
主要方法:
- 开发了一种混合架构,将模块化感知和控制与数据驱动路径规划相结合.
- 为无数据流和协同操作提供集成组件.
- 在2023年CARLA自动驾驶挑战赛中评估了架构.
主要成果:
- 在2023年CARLA自动驾驶挑战赛的SENSORS和MAP赛道中获得第一和第二名.
- 在SENSOR赛道的排名表2上获得第一名,赢得了比赛.
- 证明了高的路线完成率 (MAP轨道的86.03%,SENSOR轨道的85.01%).
结论:
- 拟议的混合架构有效地整合了自动驾驶的模块化和端到端方法.
- 这种新的设计在各种条件下提高了导航性能,超过了现有的方法.
- 该系统在CARLA挑战赛中的成功证明了其在现实世界自动驾驶应用中的潜力.
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