Machine learning approaches often lack safety guarantees, which are often a key
requirement in real-world tasks. This paper addresses the lack of safety guarantees by …

This paper presents a safe imitation learning approach for autonomous vehicle driving, with
attention on real-life human driving data and experimental validation. In order to increase …

For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …

Abstract Human-like decision making is crucial to developing an autonomous driving system
(ADS) with high acceptance. Inspired by the cognitive map, this paper proposes a …

In this paper, we consider the problem of autonomous lane changing for self driving vehicles
in a multi-lane, multi-agent setting. We use reinforcement learning solely to obtain a high …

The use of reinforcement learning (RL) in real-world domains often requires extensive effort
to ensure safe behavior. While this compromises the autonomy of the system, it might still be …

We propose a multi-agent based computational framework for modeling decision-making
and strategic interaction at micro level for smart vehicles in a smart world. The concepts of …

The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …

How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …