Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing

Abstract

The on-board sensors of connected autonomous vehicles (CAVs) are limited by their range and inability to see around corners or blind spots, otherwise known as non-line of sight scenarios (NLOS). These scenarios have the potential to be fatal (critical scenarios) as the sensors may detect an obstacle much later than the amount of time needed for the car to react. In such cases, mechanisms such as vehicular communication are required to extend the visibility range of the CAV. Despite there being a substantial body of work on the development of navigational and communication algorithms for such scenarios, there is no standard method for generating and selecting critical NLOS scenarios for testing these algorithms in a scenario-based simulation environment. This paper puts forward a novel method utilising a genetic algorithm for the selection of critical NLOS scenarios from the set of all possible NLOS scenarios in a particular road environment. The need to select critical scenarios is pertinent as the number of all possible driving scenarios generated is large and testing them against each other is time consuming, unnecessary and expensive. The selected critical scenarios are then validated for criticality by using a series of MATLAB based simulations.