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Leveraging Cooperative Perception for Blind Spot Mitigation in Lane-Changing Scenarios

Year: 2026
Type: article
Abstract: This paper investigates how cooperative perception can be leveraged for blind spot mitigation and to avoid potential vehicle collisions during lane-changing maneuvers in multi-lane highways. To achieve this goal, a simulation-based study of a lane-changing scenario relying on cooperative perception is comprehensively presented, where the obstructing vehicle uses vehicle-to-vehicle (V2V) communication to periodically transmit perception sensor data to the ego vehicle containing relevant state information of a vehicle approaching from the blind spot zone. For the ego vehicle, a cooperative-perception-based lane-changing system (CP-based LCh) is introduced, which uses sensor information and the received Collective Perception Messages (CPMs) to predict vehicle trajectories and assess in advance the reliability of the lane-changing maneuver. The lane-changing mechanism in the ego vehicle encompasses the following stages: most important object (MIO) identification, risk assessment and lane-changing decision, and driving behavior definition. The collision avoidance capability of the CP-based LCh system is investigated in comparison with a conventional blind spot detection (Conventional BSD) strategy, using a realistic simulation framework consisting of RoadRunner, Automated Driving Toolbox, and MATLAB/Simulink. The evaluation is conducted under different yaw angles, velocities, accelerations, transmission rates, safety margins, safety windows, probabilities of successful reception, and types of obstructing vehicles. Simulation results demonstrate that CP-based LCh consistently outperforms Conventional BSD in terms of Collision Prevention Rate (CPR) under the studied highway conditions, providing valuable insights into how cooperative perception can be leveraged to improve road safety.
Source: Electronics
DOI: 10.3390/electronics15204827
Language: English
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7222983158
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