一句话：提出 TEASER，从车辆仿真器传感器生成真实 CAN 信号，支持自动驾驶软件仿真回归测试。
自动驾驶汽车等安全关键系统需要严格测试，其电子控制单元通常通过 CAN 总线接收传感器数据。然而，人工定义能真实反映车辆状态的 CAN 输入十分困难。本文提出 TEASER，一个从先进车辆仿真器传感器数据生成真实 CAN 信号的工具，用于自动驾驶软件回归测试。作者将 TEASER 集成到 aicas GmbH 的 Jenkins 持续集成流水线中，在仿真环境执行测试用例，并通过 CAN 总线将传感器数据发送到物理 CAN 设备（被测对象）。评估表明，TEASER 能生成并执行暴露仿真故障的 CI 测试用例，且产生的 CAN 输入能真实表示现实世界中的自动驾驶汽车状态，从而提升基于仿真的 CAN 总线回归测试的自动化与有效性。
Safety-critical systems such as self-driving cars (SDCs) must be rigorously tested. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge for SDC maintainers and testers is the need to manually define the CAN inputs that realistically represent the state of the SDC in the real world. To address this challenge, we developed TEASER; a tool that generates realistic CAN signals for SDCs obtained from sensors from state-of-the-art car simulators. We evaluated TEASER based on its integration capability into a DevOps pipeline of aicas GmbH, a company in the automotive sector. Concretely, we integrated TEASER in a Continous Integration (CI) pipeline configured with Jenkins. The pipeline executes the test cases in simulation environments and sends the sensor data over the CAN bus to a physical CAN device, the test subject. Our evaluation shows the ability of TEASER to generate and execute CI test cases that expose simulation-based faults (using regression strategies); the tool produces CAN inputs that realistically represent the state of the SDC in the real world. This result is critically important for increasing the automation and effectiveness of simulation-based CAN bus regression testing for SDCs.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{BirchlerRKGLHKP23,
title = {TEASER: Simulation-Based CAN Bus Regression Testing for Self-Driving Cars Software},
author = {Christian Birchler and Cyrill Rohrbach and Hyeongkyun Kim and Alessio Gambi and Tianhai Liu and Jens Horneber and Timo Kehrer and Sebastiano Panichella},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00154}
}