On DriverVehicleEnvironment Integration for MultiActuated Ground Vehicles Safety Advancement: An Overview of the Interdisciplinary Training Network in MultiActuated Ground Vehicles Transportation systems are invariably burdened with dynamically changing environmental conditions and illdefined human factor. To raise ground vehicle safety on a new supreme level and to boost autonomous vehicles development drivervehicleenvironment cooperation is inevitable. In this paper, an overview of several existing drivervehicleenvironment integration methods with purpose of vehicle safety enhancement are stressed. Five unique and fundamentally different solutions are proposed, which have common similarity: the solutions are accomplished with machine learning algorithms. The methods aim at modelling driversu0027 or vehiclesu0027 behaviour with reasonable prediction accuracy under various complex scenarios. All five solutions are developed in individual projects in a framework of a continuous interdisciplinary European network ITEAM. The aim of the paper is to underline significant benefit of manmachineenvironment integration in vehicle safety systems by exploiting fairly received tremendous attention machine learning methods.

Jointly Learnable Behavior and Trajectory Planning for SelfDriving Vehicles The motion planners used in selfdriving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior planner, which handles highlevel decisions and produces a coarse trajectory, and trajectory planner that generates a smooth, feasible trajectory for the duration of the planning horizon. These planners, however, are typically developed separately, and changes in the behavior planner might affect the trajectory planner in unexpected ways. Furthermore, the final trajectory outputted by the trajectory planner might differ significantly from the one generated by the behavior planner, as they do not share the same objective. In this paper, we propose a jointly learnable behavior and trajectory planner. Unlike most existing learnable motion planners that address either only behavior planning, or use an uninterpretable neural network to represent the entire logic from sensors to driving commands, our approach features an interpretable cost function on top of perception, prediction and vehicle dynamics, and a joint learning algorithm that learns a shared cost function employed by our behavior and trajectory components. Experiments on realworld selfdriving data demonstrate that jointly learned planner performs significantly better in terms of both similarity to human driving and other safety metrics, compared to baselines that do not adopt joint behavior and trajectory learning.