Optimizing Automated Driving Safety, Comfort, and Efficiency through Prediction Horizon Analysis
The choice of prediction horizon can significantly impact the safety, comfort, and efficiency of automated vehicles. This study explores the relationship between different prediction horizons and vehicle-level performance to determine the minimum required and optimal prediction horizons for specific automated driving applications.
Validation of Shared Control Strategies for Critical Maneuvers in Automated Driving Systems
This research presents the validation of shared control strategies for critical maneuvers, including overtaking in low visibility and lateral evasive actions, in automated driving systems. The proposed approach focuses on the lateral control of the vehicle and involves a modular architecture with an arbitration module and shared control algorithms. The validation is conducted using a dynamic simulator with real drivers, demonstrating improved safety and user acceptance compared to no shared-control support.
Causal Analysis and Transfer of Driving Scenarios to Unseen Intersections
This paper proposes a methodology to systematically analyze causal relations between parameters of driving scenarios in order to decrease the amount of required data and to transfer causal patterns for generating realistic scenarios on unobserved urban intersections.
Efficient Risk-aware Branch MPC for Automated Driving
Proposing a risk-aware motion planning framework to address uncertainty in automated driving scenarios.
Understanding Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
Enhancing trust in automated driving through qualitative scene understanding and explanations.
Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving
Introducing PEP, a lightweight equivariant planning model that integrates prediction and planning in a joint approach.
Trustworthy Automated Driving: Qualitative Scene Understanding and Explanations
Enhancing automated driving trust through qualitative scene understanding with QXG.
