Enhancing Sensor Capabilities of Open-Source Simulation Tools to Support Autonomous Vehicles Safety Validation

Abstract

Autonomous Vehicles (AVs) are expected to provide relevant benefits to the society in terms of safety, efficiency and accessibility. However, AVs are safety-critical systems, and it is mandatory to assure that they are going to be safe when operating on public roads. However, the safety of AV is still an open, and challenging issue. A combination of simulation, test track, and on-road testing approaches is being recommended to validate the AV safety performance. Testing AVs in real-world scenarios is a widely used, but neither an efficient nor a safe approach to validate safety. Therefore, simulation-based approaches are demanded. Motivated by this challenge, we have developed a simulation-based safety analysis framework, based on open-source tools, to be applied to the future of the road transportation systems. However, the open-source tools we have adopted for the framework have limitations to model real-world elements, especially perception sensors. We thus here present the extensions made to these open-source tools, focused on the development of a perception sensor model in the native OpenDS tool, which enables detecting obstacles around the vehicle, considering the same main characteristics observed in Radar and LiDAR sensors. As the main conclusion, these tools enhancements have improved the simulation-based safety analysis framework capabilities for modeling, simulating and analyzing – in a more precise way and for safety validation purposes – the behavior of AV in simulated traffic scenarios when different embedded detection sensor characteristics are considered in its deployment.