A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Intelligent Transportation Systems Year : 2018

A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities

Abstract

Urban environments are among the most challenging scenarios for car-following systems, since pedestrians may interfere with the platoon unexpectedly. To address this problem, this paper proposes a cooperative system using vehicle-to-vehicle and vehicle-to-pedestrian communication links. A fractional-order control-based cooperative adaptive cruise control benefits of communication for tighter inter-vehicle distances, while pedestrian communication is fused with LiDAR sensing to allow the detection of occluded pedestrians. The prediction of the pedes-trians' trajectories is used to perform a speed reduction or an emergency braking that interrupts the car-following yif necessary. Whenever a platoon decoupling occurs, a gap-closing maneuver is executed so that the ego-vehicle rejoins the platoon in a string stable way. The complete system was tested on experimental platforms at inria facilities, providing encouraging results and demonstrating the correct performance of the integrated systems. Index Terms— Cooperative systems, fractional calculus, intelligent transportation system (ITS), sensor fusion, collision avoidance system.
Fichier principal
Vignette du fichier
CACC_EB_V2P.pdf (3.25 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01835121 , version 1 (11-07-2018)

Identifiers

Cite

Carlos Flores, Pierre Merdrignac, Raoul de Charette, Francisco Navas, Vicente Milanés, et al.. A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities. IEEE Transactions on Intelligent Transportation Systems, 2018, pp.1 - 10. ⟨10.1109/TITS.2018.2841644⟩. ⟨hal-01835121⟩
145 View
846 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More