33 citations · 127 across the 9 of their papers we have counts for
9 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…
Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty
Julian Bernhard, Patrick Hart, Amit Sahu +2
Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and plann…
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…
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
Julian Bernhard, Stefan Pollok, Alois Knoll
For highly automated driving above SAE level~3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the v…
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving
Julian Bernhard, Alois Knoll
Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these…
Robust Stochastic Bayesian Games for Behavior Space Coverage
Julian Bernhard, Alois Knoll
A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted w…
BARK: Open Behavior Benchmarking in Multi-Agent Environments
Julian Bernhard, Klemens Esterle, Patrick Hart +1
Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that intera…
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…