This article describes the design, implementation, and evaluation of an active lane change
control algorithm for autonomous vehicles with human factor considerations. Lane changes …

Detection of merge scenarios by an autonomous vehicle (AV) is disclosed. A system
includes a memory and a processor. The memory includes instructions executable by the …

Accurate and robust recognition and prediction of traffic situation plays an important role in
autonomous driving, which is a prerequisite for risk assessment and effective decision …

Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent
systems. This is especially critical for planning in complex multi-agent environments, where …

Action anticipation is critical in scenarios where one needs to react before the action is
finalized. This is, for instance, the case in automated driving, where a car needs to, eg, avoid …

Deep reinforcement learning (DRL) has a great potential for solving complex decision-
making problems in autonomous driving, especially in mixed-traffic scenarios where …

This study models the decision-making characteristics of a driver regarding whether he
accepts a merging car at a highway junction. Then, the application of the modeling to the …

Highly automated driving requires effective handling of many complex scenarios. Here we
study a specific important task of highly automated driving: merging into traffic from a …

In order to achieve safe and high-quality decision-making and motion planning, autonomous
vehicles should be able to generate accurate probabilistic predictions for uncertain behavior …

Prior work has looked at applying reinforcement learning (RL) approaches to autonomous
driving scenarios, but the safety of the algorithm is often compromised due to instability or …