The way to autonomous driving is closely connected to the capability of verifying and validating Advanced Driver Assistance Systems (ADAS), as it is one of the main challenges to achieve secure, reliable and thereby socially accepted self-driving cars. Hardware-in-the-Loop (HiL) based testing methods offer the great advantage of validating components and systems in an early stage of the development cycle, and they are established in automotive industry. When validating ADAS using HiL test benches, engineers face different barriers and conceptual difficulties: How to pipe simulated signals into multiple sensors including radar, ultrasonic, video, or lidar? How to combine classical physical simulations, e.g. vehicle dynamics, with sophisticated three-dimensional, GPU-based environmental simulations? In this article, we present current approaches of how to master these challenges and provide guidance by showing the advantages and drawbacks of each approach. Therefore, we discuss different ADAS setups and show ways of how to implement HiL test benches for these. We discuss two categories: 1) Hardware level: we focus on the communication structure between the simulated plant model and the Unit under Test (UuT). We show possible interfaces into the sensor units and involved bus systems. 2) Software level: we focus on how to provide the data the UuT expects. This results in rendering images, creating data lists or providing ray-tracing based point clouds. This article provides solutions for current and up-coming challenges when dealing with HiL-based validation of ADAS and presents an overview of current test-approaches.
The way to autonomous driving is closely connected to the capability of verifying and validating Advanced Driver Assistance Systems (ADAS), as it is one of the main challenges to achieve secure, reliable and thereby socially accepted self-driving cars. Hardware-in-the-Loop (HiL) based testing methods offer the great advantage of validating components and systems in an early stage of the development cycle, and they are established in automotive industry. When validating ADAS using HiL test benches, engineers face different barriers and conceptual difficulties: How to pipe simulated signals into multiple sensors including radar, ultrasonic, video, or lidar? How to combine classical physical simulations, e.g. vehicle dynamics, with sophisticated three-dimensional, GPU-based environmental simulations? In this article, we present current approaches of how to master these challenges and provide guidance by showing the advantages and drawbacks of each approach. Therefore, we discuss different ADAS setups and show ways of how to implement HiL test benches for these. We discuss two categories: 1) Hardware level: we focus on the communication structure between the simulated plant model and the Unit under Test (UuT). We show possible interfaces into the sensor units and involved bus systems. 2) Software level: we focus on how to provide the data the UuT expects. This results in rendering images, creating data lists or providing ray-tracing based point clouds. This article provides solutions for current and up-coming challenges when dealing with HiL-based validation of ADAS and presents an overview of current test-approaches.
