LAUSR

Scenario-based testing is already a well-known test approach to the automotive industry for the Validation, Verification and Testing of Connected and Automated Vehicles. How to construct the scenarios of complex traffic environment is a major challenge to this method. A common approach for generating scenarios is collecting and post-processing natural traffic data with a lot of time and money costs. In order to reduce the cost of scenario collection, we propose a low-cost method based on Microscopic Traffic Simulation to obtain a large number of urban traffic scenarios. Based on public data, we establish a microscopic traffic model for a specific area within the Shenzhen urban. Through a simulation, the 24-hour traffic behavior of vehicles for this area is simulated. About 189,752 scenarios covering the entire travel process are generated, which is equivalent to the data collected by a well-equipped car traveling 254,480 kms or 8,288 h. Our scenarios include not only typical urban scenarios such as U-turn and Parallel-driving (two vehicles driving in parallel on a single lane), but also collision accident scenarios of various forms. In addition, in order to evaluate the risks faced by Connected and Automated Vehicles in different scenarios, we design a new criticality metric, Scenario Risk Index, based on the risk assessment principle. The Scenario Risk Index has nothing to do with a certain function, and can quantitatively comprehensively evaluate the criticality and loss of potential accidents.