Connected and Automated Vehicles

Autonomous Vehicle Safety Performance in Mixed Traffic: Insights from NHTSA Crash Data

Mahdinia, Iman
Julia Griswold
Erz, Tristan
2026

The safe deployment of autonomous vehicles (AVs) depends on the ability of automated driving systems (ADS) to handle rare, complex, and safety-critical “edge cases.” This study develops a novel framework for identifying and analyzing such scenarios using the National Highway Traffic Safety Administration (NHTSA) ADS crash dataset. Two complementary approaches are applied. First, large language models (LLMs) are used to directly analyze crash narratives, identifying edge cases through high-risk keyword and phrase detection and anomaly-based rarity analysis. Second, LLMs are employed to...

Staging at the Curb: Evaluating the Impacts of Shared Automated Vehicle Fleet Operations Under Curb Usage Restrictions

Bahk, Younghun
Hyland, Michael
Susan Shaheen
Wolfe, Brooke
Adam Cohen
2026

Shared automated vehicle (SAV) ridehailing services are now operating in several metropolitan regions in the United States. While providing benefits, SAV services may exacerbate issues related to curb usage and vehicle kilometers traveled (VKT) in urban areas. The objective of this study is to provide guidance to cities by evaluating the impacts of SAVs’ short-term curb usage for staging between serving ride requests, under different curb restrictions and SAV operational strategies. We focus on the following performance metrics: VKT, curb productivity, customer wait time, and customer...

Curb Staging: Understanding the Impacts of Automated Vehicle Ridehail Fleet Operations Under Different Parking Policies

Bahk, Younghun
Hyland, Michael
Susan Shaheen
Wolfe, Brooke
Adam Cohen
2025

Automated vehicle (AV) ridehailing services are now operating in several metropolitan regions in the United States. While providing benefits, AV ridehailing services may exacerbate issues related to curb usage and vehicle kilometers traveled (VKT) in urban areas. The objective of this study is to provide guidance to cities by evaluating the impacts of AVs' short-term curb usage for staging between serving ride requests. We focus on the following performance metrics: VKT, curb productivity, customer wait time, and customer matching rate. To perform the analysis, we use a high-fidelity...

Navigating Seismic Shifts in Transportation

Susan Shaheen
Adam Cohen
2024

In the coming decades, converging innovations and technologies are likely to play a transformative role in transportation. In particular, the commodification of transportation coupled with vehicle automation will likely result in fundamental changes to cities by altering the built environment, costs, commute patterns, and modal choice. Naturally, vehicle automation will not inherently solve today's transportation challenges. To solve these challenges, the convergence of these mobility innovations requires thoughtful planning and public policies that balance societal goals with commercial...

Shared Mobility Services: Prioritizing Social Good

Susan Shaheen
Adam Cohen
2024

Access to transportation is integral to enhancing opportunities for employment, education, health care, and recreation. SAVs will not fundamentally solve today's transportation challenges. To solve them, AVs require thoughtful planning and public policies that balance societal goals with commercial interests. To harness and maximize the social and environmental benefits of highly automated vehicles, we need to prepare for a multiphase transition toward highly automated vehicles today. If driver-less vehicles are thoughtfully implemented with access and social/racial equity in mind,...

The Impacts of Shared and Automated Mobility

Susan Shaheen
Adam Cohen
2024

This chapter reviews findings from shared mobility studies including ride-sharing (carpooling and vanpooling), carsharing, (bikesharing and scooter sharing), and TNCs. We conclude with a discussion of shared automated vehicles (SAVs) and their potential impacts.

Shared Automated Vehicle Toolkit: Policies and Planning Considerations for Implementation 2022

Susan Shaheen
Adam Cohen
Broader, Jacquelyn
Hoban, Sarah
Auer, Ashley
Cordahi, Gustave
Kimmel, Shawn
2022

Technology is changing the way people move and is reshaping mobility and society. The integration of transportation modes, real-time information, and instant communication and dispatch—possible with the click of a mouse or the touch of a smartphone app—is redefining mobility.

The DRIVE AI Consortium

Optimal-Velocity-Based Car-Following Model With Control Lyapunov-Barrier Functions

Yeo, Yuneil
Bonsanto, Pietro
Miti, Masuma Mollika
Maria Laura Delle Monache
2026

This paper develops an optimization-based control framework for a microscopic nonlinear car-following model. The controller is obtained from a Control Lyapunov Function-Control Barrier Function-Quadratic Programming framework that enforces stability, velocity feasibility, and collision-avoidance constraints while minimizing control effort. The resulting controller mitigates the limitations of the spacing-dependent singularity-based car-following models and guarantees closed-loop safety and stability.

Charging Infrastructure Demands of Shared-Use Autonomous Electric Vehicles in Urban Areas

Hongcai Zhang
Colin Sheppard
Tim Lipman
Teng Zeng
Scott Moura
2020

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM...