How Far Ahead Should Autonomous Vehicles Start Resolving Predicted Conflicts? Exploring Uncertainty-Based Safety-Efficiency Trade-Off

Resolving predicted conflicts is vital for safe and efficient autonomous vehicles (AV). In practice, vehicular motion prediction faces inherent uncertainty due to heterogeneous driving behaviours and environments. This spatial uncertainty increases non-linearly with prediction time horizons, leading AVs to perceive more road space occupied by...

PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles

Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One particular challenge is the problem of including the Sensing and Perception (S&P) subsystem into the virtual simulation loop in an efficient and effective manner....

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is...

Potential health and well-being implications of autonomous vehicles

Transportation's effects on health and well-being are widely recognized. In the near future, autonomous vehicles (AVs) are expected to revolutionize transportation options and ways of travel. Consequently, the effect of AVs on population health and well-being is a crucial topic of interest for transportation policymaking, one that has...