Collaborative Research: OAC Core: Stochastic Simulation Platform for Assessing Safety Performance of Autonomous Vehicles in Winter Seasons

The safety of an autonomous vehicle (AV) highly depends on the generalization capability of its automation systems (e.g., perception and decision-making) when being deployed in diverse physical environments. Although the current commercialization of AVs has been shown to improve traffic safety, AV safety performance under adverse driving conditions in winter seasons still lacks comprehensive evaluation.

To bridge the research gap, this project aims to develop a stochastic simulation platform, which examines the efficiency, reliability, and safety of AVs, to prevent costly mistakes in widespread field implementations. The research methods use a foundation of machine learning and physics principles to formulate an integrated and hybrid approach to model stochastic vehicle behaviors in traffic streams.

Potential AV safety risks under adverse driving conditions will be assessed with dynamic modeling of vehicle behavior. The project will produce an open-source and cloud-based simulation platform that allows public access to test vehicle automation systems. The simulation models can be improved over time through the use of an online machine learning architecture.

The research activities will be closely integrated with a set of education and outreach activities that include (i) incorporating advanced computational techniques into the curriculum, (ii) sparking the interests of younger generations in science and engineering by local K-12 outreach efforts and summer camps, and (iii) broadening the participation of underrepresented student groups in computing through the artificial intelligence club at San Diego State University, a Hispanic serving institution.

This multidisciplinary research project aims at contributing improved algorithms in simulation and fundamental knowledge in computing to building an advanced cyberinfrastructure toolkit. The project focuses on producing a stochastic simulation platform that can evaluate the capabilities of AVs' automated driving systems. The motivation is to produce a reliable tool that can model stochastic vehicle behaviors, study vehicle dynamics, and predict potential AV safety risks under adverse driving conditions in winter.

To this end, the project will first leverage the physics principles of a microscopic traffic model to regularize the machine learning process for simulating vehicle interactions. Second, both multi-vehicle and single-vehicle crash probabilities in mixed traffic will be predicted by integrating the traffic simulation model with a new vehicle dynamics model.

The stochastic vehicle motions will then be studied to assess AV safety performance on icy/snowy pavement. Third, the models will be integrated into an open-source software package with comprehensive documentation and multiple application cases. The expected deliverable will be a public cloud-based platform that is easy to access and is capable of incorporating new data streams for model improvement.

After validating the models with field data, the project will connect the simulations with existing automated driving systems for testing. The project can have broad impacts on other science and engineering fields, such as physics-supported artificial intelligence, smart and autonomous systems, and other research domains that depend on simulated data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.