Operational Design Domain Feature Optimization Route Planning Tool for Automated Vehicle Open Road Testing

Autonomous vehicles must be able to function safely in complex contexts, involving unpredictable situations and interactions. To ensure this, the system must be tested at various stages as described by the V-model. This process iteratively tests and validates distinct parts of the system, starting with small components to system level assessment. However, this framework presents challenges when adapted to deal with testing problems that face autonomous vehicles. Open road testing is an effective way to expose the system to real world scenarios in combination with specific driving situations described by the Operational Design Domain (ODD). The task of finding a path between two points that maximizes the ODD exposure is not a trivial task, without mentioning that in most cases, the developers must design routes in unfamiliar regions. This represents a significant effort and resources consumption, which makes it important to optimize this task. This paper approaches this problem by proposing a tool for automation support to routes design by using a dataset of road attributes in addition with a search algorithm which considers the roadway attributes required by the ODD to find a set of optimal routes. The purpose of the tool is to reduce route planning times, optimize driving milage and facilitate further analysis. To show if the tool represents an improvement, we design a set of routes defining start and end points locations in 5 different cities of the US selecting from a list of available road attributes one route attribute at a time for optimization and contrasting the tool proposed routes against references routes that only optimizes driving distance or driving time. The results of this comparison were satisfactory in the sense that in every case the tool proposed routes that improved the selected attribute exposure.