17 citations · 24 across the 3 of their papers we have counts for
7 papers · 1 filter
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…
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation
Klemens Esterle, Tobias Kessler, Alois Knoll
Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical co…
Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving
Tobias Kessler, Julian Bernhard, Martin Buechel +7
Although many research vehicle platforms for autonomous driving have been built in the past, hardware design, source code and lessons learned have not been made available for the n…