Optimization of Vehicular Trajectories under Gaussian Noise Disturbances

Abstract

Nowadays, research on Vehicular Technology aims at automating every single mechanical element of vehicles, in order to increase passengers' safety, reduce human driving intervention and provide entertainment services on board. Automatic trajectory tracing for vehicles under especially risky circumstances is a field of research that is currently gaining enormous attention. In this paper, we show some results on how to develop useful policies to execute maneuvers by a vehicle at high speeds with the mathematical optimization of some already established mobility conditions of the car. We also study how the presence of Gaussian noise on measurement sensors while maneuvering can disturb motion and affect the final trajectories. Different performance criteria for the optimization of such maneuvers are presented, and an analysis is shown on how path deviations can be minimized by using trajectory smoothing techniques like the Kalman Filter. We finalize the paper with a discussion on how communications can be used to implement these schemes.

Inter-module Communications for Rear-end Collision Avoidance Messaging in an IEEE 802.11p Interface

“…As future work, we plan to extend the functionality of the supported application in the bidirectional communications' scheme by also implementing evasive maneuvering for collision avoidance policies. Actually, a recently published algorithm for this type of collision avoidance scenarios has been developed by the Authors (Tomas-Gabarron et al, 2013), giving promising results, which now can be structured in an application in NCTUns in order to assess the performance of the communication process in this particular implementation.…”