Fault Tolerance Analysis of Car-Following Models for Autonomous Vehicles

Microscopic car-following models can be applied in Autonomous Vehicles (AVs) to control the real-time longitudinal interactions among individual vehicles. Moreover, they can have a vital role in Advanced Vehicle Control and Safety Systems (AVCSSs) such as collision warning, adaptive cruise control, lane guidance driver assistance, brake assist as well as in modeling simulation of safety studies and capacity analysis in transportation science. In reality, sensor measurements are generally inaccurate. Surprisingly no comparative assessment of the car-following models in presence of sensor (measurement) errors for AVs exist till now to the best of our knowledge. Therefore, in this paper we evaluate the prominent car-following models for Avs in presence of sensor errors in terms of safety, trip times, flow and fuel efficiency through simulations. We show that sensor errors significantly impact safety and flow in all models, while they do increase trip times and fuel consumption of some of the models. None of the models is completely fault-tolerant and suitable for AVs as some models produce collisions and/or negative velocity while all models violate traffic light. Nonetheless, the k-leader Fuel-efficient Traffic Model (kFTM) is the most fault-tolerant negative velocity and collision free model having reasonable trip times and energy consumption among the investigated models.