Vision-Based Blind Spot Warning System by Deep Neural Networks

Virgilio, R G Víctor; Sossa, Humberto; Zamora, Erik

doi:10.1007/978-3-030-49076-8_18

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…This interest pertains not only to the intelligent functions that support a driver during driving but also to automated driving. As road accidents occur frequently and result in property damages, injury, and even death, all of which impose a high cost to societies, the main aim in developing new and more sophisticated functions for ADAS systems is to improve road safety, to mitigate traffic issues, and to improve driving comfort [5,6].…”

Section: Introductionmentioning

confidence: 99%

Optimal H∞ Control for Lateral Dynamics of Autonomous Vehicles

Gagliardi

Lupia

Cario

et al. 2021

Sensors

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This paper presents the design and validation of a model-based H∞ vehicle lateral controller for autonomous vehicles in a simulation environment. The controller was designed so that the position and orientation tracking errors are minimized and so that the vehicle is able to follow a trajectory computed in real-time by exploiting proper video-processing and lane-detection algorithms. From a computational point of view, the controller is obtained by solving a suitable LMI optimization problem and ensures that the closed-loop system is robust with respect to variations in the vehicle’s longitudinal speed. In order to show the effectiveness of the proposed control strategy, simulations have been undertaken by taking advantage of a co-simulation environment jointly developed in Matlab/Simulink © and Carsim 8 ©. The simulation activity shows that the proposed control approach allows for good control performance to be achieved.

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Section: Introductionmentioning

confidence: 99%