Design of an Improved Fuzzy Inference-Based Emergency Obstacle Avoidance Control System for Intelligent Vehicles

Remote driving system development commonly known as intelligent transportation systems present a crucial field of study where designers focus on autonomous and intelligent vehicle security. The current obstacle avoidance approaches encounter difficulties while handling unpredictable and changing road situations which results in doubtful decisions along with critical safety problems. An Improved Fuzzy Inference-Based Emergency Obstacle Avoidance Control System should be applied to intelligent vehicles according to this research to overcome current obstacles. Fuzzy logic operations in the proposed system manage uncertain data alongside an optimized control system which adapts automatically to environmental changes for enhancing both safety and efficiency of vehicle avoidance processes. The system detects obstacles and evaluates possible collision dangers through the integration of vision-perception sensors together with ultrasonic detectors. The fuzzy inference system uses FIS procedure to interpret ambiguous information from which it produces autonomous system commands to initiate emergency measures. The proposed system went through complete simulation testing as well as direct field experiments to establish its operating effectiveness. This shows that the obstacle detection accuracy improves and the emergency response and vehicle trajectory planning time reduce by using the improved fuzzy inference-based approach. The implemented system delivers stronger stability performance and accelerates response times as well as minimizes overshoot compared to traditional obstacle avoidance systems when operating at high speeds.