Analysis and development of fault-tolerant car-following models for communication-enabled autonomous vehicles

Abstract

Microscopic car-following models can be applied in Autonomous Vehicles (AVs) to control the real-time longitudinal interactions among individual vehicles. Besides, car- following models can have a vital role in Advanced Vehicle Control and Safety Systems such as collision warning, adaptive cruise control, lane guidance driver assistance, and brakeassist,aswellasinmodelingsimulationofsafetystudiesandcapacityanalysis intransportationscience.Thecar-followingmodelsrelyonsensor-measuredvalues.How- ever,inreality,sensormeasurementsaregenerallyinaccurate.Surprisingly,tillnowtothe best of our knowledge, no assessment of the car-following models in the presence of sensor measurement errors for AVs exists. To fill up this gap in the literature, in this thesis, we assess nine prominent car-following models for AVs in the presence of sensor measurement errorsintermsofsafety,triptimes,andflowandfuelefficiencythroughrigoroussimula- tionscoveringarealhighwaymap.Weshowthatsensorerrorssignificantlyandnegatively impactsafetyandflowinallmodels,whiletheydodegradetransportefficiency(increase triptimesandfuelconsumptionofsomeofthemodels).Moreover,animportantfinding ofourstudyisthatnoneofthemodelsishighlyfault-tolerantand suitableforAVsinthe presenceofsensormeasurementerrors.Thishappensassomemodelsproducecollisions and/or negative velocity while all models violate traffic lights in the presence of sensor measurement errors. Nonetheless, we find that the k-leader Fuel-efficient Traffic Model (kFTM) is the most fault-tolerant and the most collision-free model, having reasonable trip times and fuel consumption among our investigated models. Additionally, to the best of our knowledge, the car-following models available to dateareyettorealizeandaccommodatetheimpactsofthesensormeasurementerrors.There-

fore, in this thesis, we propose a new fault-tolerant car-following model by realizing and accommodating the sensor measurement errors. We evaluate the proposed fault-tolerant car-followingmodelinthepresenceofsensorerrorsbasedonsafetyandtransportefficiency through rigorous simulations covering a couple of real highway maps. The proposed model improvesthelevelofsafetyby97%.However,itcannotnullifythenumberofcollisions. Therefore, we further propose three model-agnostic strategies to escalate the fault tolerance levels of the car-following models. We evaluate the proposed strategies in the presence of sensor errors based on safety and transport efficiency through extensive simu- lationscoveringacoupleofrealhighwaymaps.Oursimulationresultsdemonstratethat the proposed strategies can greatly reduce (or even nullify in most cases) the number of collisions that occur for different car-following models in the presence of sensor errors atthecostofminimaldegradationintransportefficiency.