45 citations · 185 across the 10 of their papers we have counts for
10 papers
PlanNetX: Learning an Efficient Neural Network Planner from MPC for Longitudinal Control
Jasper Hoffmann, Diego Fernandez, Julien Brosseit +5
Model predictive control (MPC) is a powerful, optimization-based approach for controlling dynamical systems. However, the computational complexity of online optimization can be pro…
Spatiotemporal motion planning with combinatorial reasoning for autonomous driving
Klemens Esterle, Patrick Hart, Julian Bernhard +1
Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multip…
Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming
Tobias Kessler, Klemens Esterle, Alois Knoll
Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of ev…
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning
Julian Bernhard, Robert Gieselmann, Klemens Esterle +1
Interaction-aware planning for autonomous driving requires an exploration of a combinatorial solution space when using conventional search- or optimization-based motion planners. W…
Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning
Klemens Esterle, Luis Gressenbuch, Alois Knoll
Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic par…
Formalizing Traffic Rules for Machine Interpretability
Klemens Esterle, Luis Gressenbuch, Alois Knoll
Autonomous vehicles need to be designed to abide by the same rules that humans follow. This is challenging, because traffic rules are fuzzy and not well defined, making them incomp…