Sensors based Lane Keeping and Cruise Control of Self Driving Vehicles

The safety and security of self-driving vehicles is challenging considering large types of sensors and computational units developed by multiple manufacturers. In this paper, a method of identifying the optimal combination of sensors, methods and algorithms are presented for the safety of lane keeping and cruise control functionality of self-driving vehicles. Moreover, an adaptive model predictive control approach is presented that incorporates optimal number of sensors to improve the performance of such vehicles. Results indicate that the presented approach could improve the safety of lane keeping and cruise control functionality as compared to other approaches. This work could pave the way for the future smart and safe self-driving transportation systems.