Evaluation of Car-Following Model for Mixed Autonomous and Human Driven Vehicles on Road Facilities.

Abstract

Driverless cars are emerging slowly but bear the opportunity to improve the traffic system efficiency and user comfort. For the near future, a mix of human-driven and driver-less vehicle co- existence is inevitable. The quest to integrate autonomous and human-driven vehicles has created numerous critical questions in the road traffic system: How can autonomous and human- driven vehicles co-exist efficiently? What are the prospects of breaching the existing car-following model widely adopted to control the longitudinal movement of vehicles and control the driver’s maintenance of a safe distance? There is no doubt, that the emergence of autonomous vehicles and their integration is altering the existing traffic theories and the idea behind the car-following model. Currently, researchers are working hard to efficiently describe the physics behind the traffic flow theory of car-following models in mixed traffic. This work investigated a homogeneous car-following model sensitivity with varying human drivers’ reaction time in mixed traffic. The ability to predict the optimal vehicle response time in a platoon to the behaviour of its predecessor is vital in predicting the effect a change in the driving environment of mixed traffic has on traffic flow. The major complexities surrounding the car- following model in a mixed traffic flow environment are how the traffic participants share the road intersection space and the car-following behavior of human-driven vehicles. Investigation in microscopic traffic flow describes the way a single car be- haves and then introduces the impact another car has on a car’s behavior to address the mixed traffic flow. The traffic speed performance under the different traffic conditions suggests that the proposed mixed traffic car-following model outperforms the existing car-following model for human-driven vehicles with an average of 22.59mph. There is a significant throughput improvement in a mixed traffic environment when the distance headway of autonomous vehicles is adjusted to support the research hypothesis.