论文标题

基于场景的自动驾驶系统测试的压力测试方法

Stress Testing Method for Scenario Based Testing of Automated Driving Systems

论文作者

Nalic, Demin, Li, Hexuan, Eichberger, Arno, Wellershaus, Christoph, Pandurevic, Aleksa, Rogic, Branko

论文摘要

测试SAE 1和2的自动化车辆测试的经典方法和程序是基于具有特定操作的定义场景,具体取决于测试的功能。对于SAE 3级以上的自动驾驶系统(AD),场景空间是无限的,要求进行虚拟测试和验证。但是,即使在模拟中,广告的与安全相关的方案的产生也很昂贵且耗时。这导致对随机和现实的交通模拟的需求。因此,微观交通流仿真模型(TFSM)已成为基于方案的AD测试的关键部分。在本文中,使用了多体模拟软件IPG汽车制造商与微观交通流仿真软件(TFSS)PTV Vissim之间的共同模拟。尽管TFSS可以提供​​交通参与者的现实和随机行为,但很少发生安全至关重要的情况(SC)。为了避免这种情况,引入了一种新型的应力测试方法(STM)。通过这种方法,通过测试车辆附近的PTV Vissim的外部驾驶员DLL接口来操纵交通参与者,以激发来自奥地利公路上统计事故数据的关键操作。这些外部驾驶员模型模仿了人类驾驶错误,从而增加了关键安全情景。结果,提出的STM方法有助于增加与安全相关的方案,以验证,测试和评估ADS。

Classical approaches and procedures for testing of automated vehicles of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For automated driving systems (ADS) of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. However, even in simulation, the generation of safety-relevant scenarios for ADS is expensive and time-consuming. This leads to a demand for stochastic and realistic traffic simulation. Therefore, microscopic traffic flow simulation models (TFSM) are becoming a crucial part of scenario-based testing of ADS. In this paper, a co-simulation between the multi-body simulation software IPG CarMaker and the microscopic traffic flow simulation software (TFSS) PTV Vissim is used. Although the TFSS could provide realistic and stochastic behavior of the traffic participants, safety-critical scenarios (SCS) occur rarely. In order to avoid this, a novel Stress Testing Method (STM) is introduced. With this method, traffic participants are manipulated via external driver DLL interface from PTV Vissim in the vicinity of the vehicle under test in order to provoke defined critical maneuvers derived from statistical accident data on highways in Austria. These external driver models imitate human driving errors, resulting in an increase of safety-critical scenarios. As a result, the presented STM method contributes to an increase of safety-relevant scenarios for verification, testing and assessment of ADS.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源