4 papers
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
Camiel Oerlemans, Bram Grooten, Michiel Braat +3
Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a gro…
SDS++: Online Situation-Aware Drivable Space Estimation for Automated Driving
Manuel Muñoz Sánchez, Gijs Trots, Robin Smit +4
Autonomous Vehicles (AVs) need an accurate and up-to-date representation of the environment for safe navigation. Traditional methods, which often rely on detailed environmental rep…
Characterization and Mitigation of Insufficiencies in Automated Driving Systems
Yuting Fu, Jochen Seemann, Caspar Hanselaar +4
Automated Driving (AD) systems have the potential to increase safety, comfort and energy efficiency. Recently, major automotive companies have started testing and validating AD sys…
Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency
Manuel Muñoz Sánchez, Chris van der Ploeg, Robin Smit +3
Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these…