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Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

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.