This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

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In the last years, two main ICT enablers have been massively explored to enforce vehicular safety: GNSS-based positioning and wireless communications. In fact, by exchanging information on their respective positions, vehicles are expected to be timely aware of the hazards around them and, consequently, to be able to promptly react, hopefully decreasing the rate of accidents. Both wireless communications and positioning are unfortunately far from being ideal, especially on board: however, as recently proposed, they may both significantly benefit from the integration of the emerging Light-Fidelity (Li-Fi) technology. The paper discusses the Li-Fi perspective, shedding light on its potential benefits and issues, also with practical consideration.

The aim of an ATR system is to remove the role of human beings from the process of target detection and recognition and