Cooperative Lane Keeping Assist: Design and Evaluation of a V2V Lane Perception Sharing Approach

Brahim El Boukili; Mohammed-Hicham Zaggaf; Lhoussain Bahatti

doi:10.18196/jrc.v6i5.26784

Authors

Brahim El Boukili University of Hassan II https://orcid.org/0009-0004-5033-9608
Mohammed-Hicham Zaggaf University of Hassan II https://orcid.org/0009-0006-9640-1563
Lhoussain Bahatti University of Hassan II https://orcid.org/0000-0001-6569-5913

DOI:

https://doi.org/10.18196/jrc.v6i5.26784

Keywords:

Lane Keeping Assist, Vehicle-to-Vehicle Communication, Autonomous Vehicles, Sensor Fusion, Confidence-weighted Averaging, Adverse Weather, Cybersecurity

Abstract

Even autonomous vehicles are becoming very advanced. Adverse weather conditions, unclear lane markings, and unexpected obstacles can still pose challenges, especially to lane keeping assist systems. The performance of these systems varies between vehicles depending on sensor quality, environmental conditions, and data processing algorithms. A focused solution to improve lane keeping capability is vehicle-to-vehicle (V2V) communication. V2V enables vehicles to share real-time information on speed, position, direction, etc. In this paper, V2V is used specifically to share lane marking data from a front vehicle to a following vehicle. These data are fused with local perception using a confidence-weighted averaging method, where each lane position input is assigned a confidence score. When local perception degrades, such as during poor weather, this approach improves lane keeping by relying on the more reliable lane marking positions of the front vehicle. We validate our V2Venhanced LKA system using MATLAB/Simulink simulations with one front vehicle. Results show up to a 92.75% reduction in mean error compared to standard LKA and smoother steering. Since the system shares only lane marking positions for lane keeping purposes, the communication load remains low. However, attention must still be given to cybersecurity aspects, as even limited data exchange via V2V is vulnerable to threats such as spoofing or tampering, which could compromise the safety of the lane keeping function.

Author Biographies

Mohammed-Hicham Zaggaf, University of Hassan II

received the Ph.D. degree in controlengineering from theUniversité Hassan II, Casablanca, Morocco, in 2019. Currently, he is Assistant Professor atthe Ecole Normale Supérieure d’EnseignementTechnique (ENSET), Hassan II university, Mohammedia, Morocco. Hisresearch interests include optimization, and nonlinearcontrol of AC machines and energy systems. He can be contacted at email: h.zaggaf@gmail.com.

Lhoussain Bahatti, University of Hassan II

received the Aggregation Electr. Engin. degree in 1995 from
the ENS CACHAN France and the DEA diploma in information processing in 1997. In 2013,
he had his Ph.D. degree in Signal and information processing from the Faculty of Science and
Technology of Mohammedia (FSTM) and qualified to Direct Research in 2016. Published
more than 60 research publications in various, national and international conference
proceedings and Journals on signal and image processing, machine learning, classification,
and control, also has experience in teaching it since 1995. He was the coordinator of the
“Electrical Engineering and Industrial Systems Control” section at ENSET (2015–2018) and
head of the “Parallel Architectures, Image and Signal Processing” research team (APTSI) at
“Disturbed Systems Signals and Artificial Intelligence” laboratory (SSDIA) (2016–2020).
Since 2018, Head of Electrical Engineering at ENSET and member of the” Electrical
Engineering and Intelligent Systems” (IESI) laboratory and head of the “Signals, Images, and
Intelligent Systems” (SISI) team since 2020. His research is also focused on the control of
renewable energy production systems and smart agriculture. He can be contacted at email:
bahatti.enset@gmail.com.

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