In this paper, a framework for assisting Connected Vehicle (CV) is proposed, with the goal of
generating optimal parameters for existing driving functions, eg, parking assistant or …

This paper presents a framework to plan and execute autonomous parking maneuvers in
complex parking scenarios. We formulate a minimum-time optimal control problem for …

In the context of automated driving, Automated Valet Parking (AVP) is a functionality that
aims at parking and retrieving vehicles in a suitably equipped infrastructure. In recent years …

This article formalizes, under a single common multisensor-based predictive control
framework, five different types of parking maneuvers: perpendicular, diagonal for both …

This article preprocesses environmental information and use it as input for the Proximal
Policy Optimization (PPO) algorithm. The algorithm is directly trained on a model vehicle in a …

Over the last decade, traffic accidents caused by human error have been accounted for 90
percent of all traffic accidents. For this reason, various active safety systems which assist …

Based on our experience in the DARPA Urban Challenge and on current trends in
consumer automobiles, we believe that driver assistance systems can be significantly …

This paper presents a study of perception and robust model-predictive control (MPC)
strategies in realistic traffic environments, which are simulated using data from real-world …

Obstacle avoidance is a fundamental operation for automated driving and its formulation
traditionally originates from robotics and decision making control fields. Given the high …

Model predictive control (MPC) is a promising approach to the lateral and longitudinal
control of autonomous vehicles. However, the parameterization of the MPC with respect to …