Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …
the last two decades. There is increasing interest in the field as the deployment of …
Formal methods to comply with rules of the road in autonomous driving: State of the art and grand challenges
We provide a review of recent work on formal methods for autonomous driving. Formal
methods have been traditionally used to specify and verify the behavior of computer …
methods have been traditionally used to specify and verify the behavior of computer …
Dense reinforcement learning for safety validation of autonomous vehicles
One critical bottleneck that impedes the development and deployment of autonomous
vehicles is the prohibitively high economic and time costs required to validate their safety in …
vehicles is the prohibitively high economic and time costs required to validate their safety in …
Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …
Barriernet: Differentiable control barrier functions for learning of safe robot control
Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …
guarantees. This article introduces a method for ensuring the safety of learned models for …
An ethical trajectory planning algorithm for autonomous vehicles
With the rise of artificial intelligence and automation, moral decisions that were formerly the
preserve of humans are being put into the hands of algorithms. In autonomous driving, a …
preserve of humans are being put into the hands of algorithms. In autonomous driving, a …
Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning
Today's self-driving vehicles have achieved impressive driving capabilities, but still suffer
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
from uncertain performance in long-tail cases. Training a reinforcement-learning-based self …
Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach
Reinforcement learning has demonstrated its potential in a series of challenging domains.
However, many real-world decision making tasks involve unpredictable environmental …
However, many real-world decision making tasks involve unpredictable environmental …
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 …
A review of testing object-based environment perception for safe automated driving
Safety assurance of automated driving systems must consider uncertain environment
perception. This paper reviews literature addressing how perception testing is realized as …
perception. This paper reviews literature addressing how perception testing is realized as …