Vehicle Dynamic Maneuverability through Data-driven Six Sigma Analysis for Enhanced Driver Assistance

This paper presents the concept of a vision-based system to provide intelligent driving assistance for steering, braking, and acceleration maneuvers. The system utilizes vision systems to detect the vehicle's surroundings, including the road, other vehicles, pedestrians, and obstacles. Based on this environmental awareness, the system can intervene to assist the driver to enhance the safety and efficiency of the driving experience by maneuverability access or alert to driver in steering, brake, and acceleration. The steering assist function analyzes the road geometry and trajectory and provides subtle steering adjustments alerts to keep the vehicle centered in the lane thereby smoothly navigate curves. The braking assist function detects potential collision threats and can assist driver on application status of brakes to avoid or mitigate impacts. Similarly, the acceleration assist function monitors the driving conditions and traffic flow to optimize throttle input for improved fuel economy, traffic flow thereby reducing the impact to dynamic parts of vehicles. A detailed six sigma analysis is done by DMAIC method to prove the proposed methodology significance compared to existing techniques.