Fog & V2V: A CARLA-Based Comparative Study of No Perception, Degraded Sensors, and Cooperative Alerts with MPC-Based Collision Avoidance

Abstract

This study investigates the safety limitations of autonomous vehicles operating under dense fog conditions, where sensor performance is severely degraded, and explores the potential of cooperative control for collision avoidance. A comparative framework is developed using the CARLA simulator to analyze four driving configurations: no perception and no communication, degraded LiDAR–radar sensing, V2V-assisted Model Predictive Control (MPC), and V2V-assisted MPC enhanced with predictive buffering. The methodology integrates fog-dependent perception modeling, cooperative hazard messaging, and real-time MPC-based longitudinal control, and evaluates system performance through multiple simulation trials under urban and highway conditions. Key performance indicators include time-to-collision, reaction time, maximum deceleration, jerk, and collision occurrence. The results demonstrate that perception-only strategies lead to late reactions and unsafe emergency braking, with minimum TTC values as low as 0.29 s and frequent collision events. In contrast, V2V-assisted MPC significantly improves anticipation and driving comfort, while the proposed predictive buffering approach achieves a 0% collision rate and increases the minimum TTC to approximately 1.93 s. The inclusion of predictive buffering further enhances robustness against communication losses, enabling smoother deceleration and consistently safe inter-vehicle spacing. Overall, the findings confirm that cooperative V2V communication combined with predictive control effectively compensates for fog-induced perception degradation and represents a viable solution for improving safety and reliability in low-visibility autonomous driving scenarios.