ITS Berkeley

Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments

Fu, Zhe
Kreidieh, Abdul Rahman
Wang, Han
Lee, Jonathan W.
Monache, Maria Laura Delle
Bayen, Alexandre M.
2023

Autonomous driving systems present promising methods for congestion mitigation in mixed autonomy traffic control settings. In particular, when coupled with even modest traffic state estimates, such systems can plan and coordinate the behaviors of automated vehicles (AVs) in response to observed downstream events, thereby inhibiting the continued propagation of congestion. In this paper, we present a two-layer control strategy in which the upper layer proposes the desired speeds that predictively react to the downstream state of traffic, and the lower layer maintains safe and reasonable...

Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

Lichtle, Nathan
Jang, Kathy
Shah, Adit
Vinitsky, Eugene
Lee, Jonathan W.
Bayen, Alexandre M.
2023

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I–24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the...

Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

Khoudari, Nour
Almatrudi, Sulaiman
Ramadan, Rabie
Carpio, Joy
Yao, Mengsha
Butts, Kenneth
Bayen, Alexandre M.
2023

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate,...

Public–Private Partnerships in Fostering Outer Space Innovations

Rausser, Gordon
Choi, Elliot
Bayen, Alexandre
2023

As public and private institutions recognize the role of space exploration as a catalyst for economic growth, various areas of innovation are expected to emerge as drivers of the space economy. These include space transportation, in-space manufacturing, bioproduction, in-space agriculture, nuclear launch, and propulsion systems, as well as satellite services and their maintenance. However, the current nature of space as an open-access resource and global commons presents a systemic risk for exuberant competition for space goods and services, which may result in a “tragedy of the commons”...

Traffic Smoothing Using Explicit Local Controllers

Hayat, Amaury
Alanqary, Arwa
Bhadani, Rahul
Denaro, Christopher
Weightman, Ryan J.
Piccoli, Benedetto
Bayen, Alexandre M.
2023

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the...

Enabling Mixed Autonomy Traffic Control

Nice, Matthew
Bunting, Matthew
Richardson, Alex
Zachár, Gergely
Lee, Jonathan W.
Bayen, Alexandre
2023

We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable...

Optimal Control of Autonomous Vehicles for Flow Smoothing in Mixed-Autonomy Traffic

Alanqary, Arwa
Gong, Xiaoqian
Keimer, Alexander
Seibold, Benjamin
Piccoli, Benedetto
Bayen, Alexandre
2023

This article studies the optimal control of autonomous vehicles over a given time horizon to smooth traffic. We model the dynamics of a mixed-autonomy platoon as a system of non-linear ODEs, where the acceleration of human-driven vehicles is governed by a car-following model, and the acceleration of autonomous vehicles is to be controlled. We formulate the car-following task as an optimal control problem and propose a computational method to solve it. Our approach uses an adjoint formulation to compute gradients of the optimization problem explicitly, resulting in more accurate and...

Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design

Jalota, Devansh
Lazarus, Jessica
Bayen, Alexandre
Pavone, Marco
2023

Credit-based congestion pricing (CBCP) has emerged as a mechanism to alleviate the social inequity concerns of road congestion pricing - a promising strategy for traffic congestion mitigation - by providing low-income users with travel credits to offset some of their toll payments. While CBCP offers immense potential for addressing inequity issues that hamper the practical viability of congestion pricing, the deployment of CBCP in practice is nascent, and the potential efficacy and optimal design of CBCP schemes have yet to be formalized. In this work, we study the design of CBCP schemes...

Connected and Automated Vehicle Technology is Not Enough; it Must also be Collaborative

Patire, Anthony D.
Dion, Francois
Bayen, Alexandre M.
2023

Connected and automated vehicles (CAVs) will revolutionize the way we travel; however, what impact this revolution will have on advancing broader societal goals is uncertain. To date, the private sector technology rollout has emphasized the automation side of CAVs and neglected the potentially transformative possibilities brought by a more collaborative notion of connectivity. This may have significant downsides from a broader societal perspective. For example, CAVs (including those on the road today) collect a vast amount of data gathered through onboard systems (e.g., radar, lidar,...

So You Think You Can Track?

Gloudemans, Derek A.
Zachár, Gergely
Wang, Yanbing
Ji, Junyi
Nice, Matthew
Bayen, Alexandre
2024

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object...