Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps
Behavior-related research areas such as motion prediction/planning, representation/
imitation learning, behavior modeling/generation, and algorithm testing, require support from …
imitation learning, behavior modeling/generation, and algorithm testing, require support from …
End-to-end interpretable neural motion planner
In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
Most autonomous driving architectures separate planning and control phases in different
layers, even though both problems are intrinsically related. Due to limitations on the …
layers, even though both problems are intrinsically related. Due to limitations on the …
Av-fuzzer: Finding safety violations in autonomous driving systems
This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …
Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction
Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …
uncertainty from the prediction originates from the noisy sensor data and from the fact that …
Lookout: Diverse multi-future prediction and planning for self-driving
In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …
Set-based prediction of traffic participants considering occlusions and traffic rules
M Koschi, M Althoff - IEEE Transactions on Intelligent Vehicles, 2020 - ieeexplore.ieee.org
Provably safe motion planning for automated road vehicles must ensure that planned
motions do not result in a collision with other traffic participants. This is a major challenge in …
motions do not result in a collision with other traffic participants. This is a major challenge in …
Dsdnet: Deep structured self-driving network
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which
performs object detection, motion prediction, and motion planning with a single neural …
performs object detection, motion prediction, and motion planning with a single neural …
Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving
In the past decades, we have witnessed significant progress in the domain of autonomous
driving. Advanced techniques based on optimization and reinforcement learning become …
driving. Advanced techniques based on optimization and reinforcement learning become …
Probabilistic prediction of vehicle semantic intention and motion
Accurately predicting the possible behaviors of traffic participants is an essential capability
for future autonomous vehicles. The majority of current researches fix the number of driving …
for future autonomous vehicles. The majority of current researches fix the number of driving …