Collaborative Autonomous Overtaking System for Mixed Traffic Environments

This paper explores the challenges associated with executing safe overtaking maneuvers. To address these concerns, we introduce a collaborative autonomous overtaking system that leverages Vehicle-to-Vehicle (V2V) communication alongside a camera-based approach for detecting surrounding vehicles. Our approach involves sharing captured images among vehicles to ensure secure overtaking, eliminating blind spots for the overtaking vehicle. The system employs potential field algorithms for path generation and model predictive control to optimize vehicle control throughout the maneuver. Through simulations in various traffic scenarios, we validate the efficacy of the proposed system, showcasing significant improvements in traffic flow and safety, notably enhancing the system safety. This research contributes to the seamless integration of autonomous systems into existing traffic setups by incorporating a camera-based vehicle detection system and reducing reliance on traditional LiDAR and radar technologies.

Authors

Institutions

Publication Details

Journal
Transportation Research Record Journal of the Transportation Research Board
Published
2024-09-26
DOI
https://doi.org/10.1177/03611981241279305
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Collaborative Autonomous Overtaking System for Mixed Traffic Environments

My Abdelouahed Sabri, Imad Lamouik, Chahinaze Ameur, Ali Yahyaouy
1 citations
Transportation Research Record Journal of the Transportation Research Board
Robotic Path Planning Algorithms
0.25
article

Collaborative Autonomous Overtaking System for Mixed Traffic Environments

My Abdelouahed Sabri, Imad Lamouik, Chahinaze Ameur, Ali Yahyaouy
article en
1 citations

Abstract

This paper explores the challenges associated with executing safe overtaking maneuvers. To address these concerns, we introduce a collaborative autonomous overtaking system that leverages Vehicle-to-Vehicle (V2V) communication alongside a camera-based approach for detecting surrounding vehicles. Our approach involves sharing captured images among vehicles to ensure secure overtaking, eliminating blind spots for the overtaking vehicle. The system employs potential field algorithms for path generation and model predictive control to optimize vehicle control throughout the maneuver. Through simulations in various traffic scenarios, we validate the efficacy of the proposed system, showcasing significant improvements in traffic flow and safety, notably enhancing the system safety. This research contributes to the seamless integration of autonomous systems into existing traffic setups by incorporating a camera-based vehicle detection system and reducing reliance on traditional LiDAR and radar technologies.

Transportation Research Record Journal of the Transportation Research BoardVol. 2679(3)
Sidi Mohamed Ben Abdellah University (MA)
Life below water
Openalex Percentile: Top 47%
Robotic Path Planning Algorithms
0.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.