Enhancing driver’s attention and overtaking efficiency in car-following model for Advanced Driver Assistance Systems (ADAS) vehicles

In the contemporary technological landscape, vehicles equipped with Advanced Driver Assistance Systems (ADAS) are expected to significantly enhance current transportation systems’ efficiency and traffic capacity. The efficiency of ADAS vehicles to have forward and backward headway is utilized in deciding a vehicle’s optimal velocity and acceleration at any time. The comprehension of nearby vehicle information plays a crucial role in anticipating traffic flow behavior, with particular effectiveness observed during overtaking maneuvers.

To comprehend how driver attention and passing can influence the velocity of the vehicle and those near, a novel car-following model is developed for Advanced Driving Assistance Systems vehicles to gain deeper insights into this phenomenon. To study the stability criterion, both “linear and nonlinear” analyses are performed for the stability conditions. A simulation for small perturbations in headway was done, and it was found that the simulated headway profile patterns (No Jam, Kink and Chaotic) for different parameter values resemble with the theoretical results. It is found that the smaller values of passing lead to the kink region by reducing the wavelength and amplitude of the kink wave, whereas a chaotic pattern is observed for higher values of passing. Traffic stability is discovered to be inversely supported by the weightage to the backward information (the headway of the preceding vehicle) for optimal velocity. Moreover, compared to the passing and backward information factors of optimal velocity, the driver’s attention on average speed of nearby vehicles is the most influential factor in stabilizing traffic. The combination of the concentration of the driver’s attention and the passing actions of drivers in ADAS vehicles have a compounding impact that affects usual driving patterns. Therefore, the improved model can be implemented as active safety technology to reduce collision accidents and other traffic-related issues.