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Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

This research focuses on the development and performance of analytical models to simulate a tractor-semitrailer in straight-ahead braking. The simulations were modified and tuned to simulate full-treadle braking with all brakes functioning correctly, as well as the behavior of the tractor-semitrailer rig under full braking with selected brakes disabled. The models were constructed in TruckSim and based on a tractor-semitrailer used in dry braking performance testing. The full-scale vehicle braking research was designed to define limits for engineering estimates on stopping distance when Class 8 air-braked vehicles experience partial degradation of the foundation brake system. In the full scale testing, stops were conducted from 30 mph and 60 mph, with the combination loaded to 80,000 lbs (gross combined weight or GCW), half payload, and with the tractor-semitrailer unladen (lightly loaded vehicle weight, or LLVW).

The Federal Side Impact Test Procedure prescribed by FMVSS 214, simulates a central, orthogonal intersection collision between two moving vehicles by impacting the side of the stationary test vehicle with a moving test buck in a crabbed configuration. While the pre- and post-impact speeds of the vehicles involved in an accident can not be duplicated using this method, closing speeds, vehicle damage, vehicle speed changes and vehicle accelerations can be duplicated. These are the important parameters for the examination of vehicle restraint system performance and the prediction of occupant injury. The acceptability of this method of testing is not as obvious for the reconstruction of accidents where the impact is non-central, or the angle of impact is not orthogonal. This paper will examine the use of crash testing with a single moving vehicle to simulate oblique or non-central collisions between two moving vehicles.