12 citations · 16 across the 13 of their papers we have counts for
13 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…
The Safety Shell: an Architecture to Handle Functional Insufficiencies in Automated Driving
C. A. J. Hanselaar, E. Silvas, A. Terechko +1
To enable highly automated vehicles where the driver is no longer a safety backup, the vehicle must deal with various Functional Insufficiencies (FIs). Thus-far, there is no widely…
Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields
Chris van der Ploeg, Truls Nyberg, José Manuel Gaspar Sánchez +2
As vehicle automation advances, motion planning algorithms face escalating challenges in achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems (ADAS)…
Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving
Manuel Muñoz Sánchez, Emilia Silvas, Jos Elfring +1
Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic beh…
Optimization-based Fault Mitigation for Safe Automated Driving
Niels Lodder, Chris van der Ploeg, Laura Ferranti +1
With increased developments and interest in cooperative driving and higher levels of automation (SAE level 3+), the need for safety systems that are capable to monitor system healt…
Scenario-based Evaluation of Prediction Models for Automated Vehicles
Manuel Muñoz Sánchez, Jos Elfring, Emilia Silvas +1
To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most a…
Informed sampling-based trajectory planner for automated driving in dynamic urban environments
Robin Smit, Chris van der Ploeg, Arjan Teerhuis +1
The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of v…
Long Horizon Risk-Averse Motion Planning: a Model-Predictive Approach
Chris van der Ploeg, Robin Smit, Arjan Teerhuis +1
Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed traffic requires improved planning methods that are risk-averse and that account for predic…
Anomaly Detection from Cyber Threats via Infrastructure to Automated Vehicle
Chris van der Ploeg, Robin Smit, Alexis Siagkris-Lekkos +2
Using Infrastructure-to-Vehicle (I2V) information can be of great benefit when driving autonomously in high-density traffic situations with limited visibility, since the sensing ca…
Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems
Chris van der Ploeg, Emilia Silvas, Nathan van de Wouw +1
Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise or varying system dynamics, such estimation is e…