CPS: Medium: Emulating Emerging Autonomous Vehicle Technologies to Understand Their Impact on Urban Congestion

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Self-driving cars are here to stay, and this emerging automated vehicle (AV) technology will transform our transportation system. Potential benefits of AV technology include improved safety and greater capacity for more vehicles to travel on the road (by forming a platoon of vehicles with very close distance with each other). But how AV technologies will evolve in the future is highly uncertain, and so is our understanding of their impacts on our transportation system. For example, none of the AV models in the literature have been validated with empirical data, which makes existing predictions about their impacts highly questionable. Recent studies on a platoon of Tesla vehicles suggest that traffic congestion might actually increase. To address this problem, this project will conduct measurements using commercially available AV vehicles and come up with mathematical models that replicate their behavior. These models will allow us to better understand how AV vehicles behave when they form a platoon with each other and come up with methods to address undesirable consequences such as congestion. The educational component of this project will expose both undergrad and graduate students to a thriving ecosystem where car manufacturers, technology companies and application developers foster innovation via open source software, learning material and data sets to train the machine learning models needed for AV technologies.The research objective of this project is to develop an analytical and numerical framework to emulate the impacts that current AV technologies will have on the transportation networks of the near future. The research approach will be based on the collection of large amounts of empirical data from Level 2/3 AVs currently on the market to train the type of machine learning models that the industry is implementing, consisting of a combination of deep neural networks and expert domain knowledge. Given the recent empirical evidence revealing that these vehicles may exhibit more string instability than human drivers, the project will identify stability constraints that can be incorporated during training to avoid instability. Additionally, the corresponding car-following models that will establish macroscopic dynamics at the network level will be formulated. The project will focus on the longitudinal acceleration/deceleration component since it plays the major role in string stability, network capacity and congestion. It also makes it possible to train machine learning models with a fraction of the data needed for general scenarios, and understanding this simplified driving scenario is the first step towards a successful analysis of more general cases. The impact of this project is expected to be significant as it will establish the connection between machine learning models and car-following models, and will steer research and development of future AV technologies towards artificial intelligence models that are guaranteed to be stable.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.