Stochastic Reachable Set Threat Assessment for Autonomous Vehicles Using Trust-Based Driver Behavior Prediction
ISSN: 2574-0741, e-ISSN: 2574-075X
Published July 06, 2022 by SAE International in United States
Citation: Khattar, V. and Eskandarian, A., "Stochastic Reachable Set Threat Assessment for Autonomous Vehicles Using Trust-Based Driver Behavior Prediction," SAE Intl. J CAV 6(2):123-137, 2023, https://doi.org/10.4271/12-06-02-0008.
Threat assessment and reliable motion prediction of surrounding vehicles are essential for proactive decision-making and ensuring safety in autonomous vehicles. Most of the vehicles on roads are human-driven, which make it difficult to predict their intentions and movements. Moreover, different driver behaviors pose different kinds of threats. Various driver behavior predictive models have been proposed in the literature. However, these models cannot be trusted entirely due to the human drivers’ highly uncertain nature. This article proposes a novel trust-based driver behavior prediction and threat assessment methodology for various dangerous situations on the road. This trust-based methodology allows autonomous vehicles to quantify the degree of trust in their predictions to generate the probabilistically safest trajectory. This approach can be instrumental in near-crash scenarios where no collision-free trajectory exists. Three different driving behaviors are considered: Normal, Aggressive, and Drowsy. Hidden Markov Models (HMMs) are used for driver behavior prediction. A “trust” in the detected driver is established by combining four driving features: longitudinal acceleration, lateral acceleration, lane deviation, and velocity. A stochastic reachable (SR) set-based approach is used to model three different driving behaviors. Short-term prediction threat (STPT) assessment is done using the probability of crash computation. This methodology can predict different driver behaviors with a certain confidence. Moreover, the proposed threat assessment methodology results in a lower rate of false positives and false negatives.