ITS Berkeley

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Chou, Fang-Chieh
Gibson, Marsalis
Bhadani, Rahul
Bayen, Alexandre M.
Sprinkle, Jonathan M.
2021

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower...

PDE Traffic Observer Validated on Freeway Data

Yu, Huan
Gan, Qijian
Bayen, Alexandre
Krstic, Miroslav
2021

This article develops a boundary observer for the estimation of congested freeway traffic states based on the Aw-Rascle-Zhang (ARZ) partial differential equations (PDEs) model. Traffic state estimation refers to the acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a model-driven approach in which the estimation of aggregated traffic states in a freeway segment is obtained simply from the boundary measurement of flow and velocity without knowledge...

Quasi-Dynamic Traffic Assignment using High Performance Computing

Chan, Cy
Kuncheria, Anu
Zhao, Bingyu
Cabannes, Theophile
Keimer, Alexander
Bayen, Alexandre
2021

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is...

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

Lee, Jonathan
Gunter, George
Ramadan, Rabie
Almatrudi, Sulaiman
Arnold, Paige
Work, Daniel B.
Seibold, Benjamin
Bayen, Alexandre
2021

This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the...

To Pool or Not to Pool? Understanding Opportunities, Challenges, and Equity Considerations to Expanding the Market for Pooling

Lazarus, Jessica
Caicedo, Juan
Bayen, Alexandre
Shaheen, Susan A
2021

On-demand mobility services such as bikesharing, scooter sharing, and transportation network companies (TNCs, also known as ridesourcing and ridehailing) are changing the way that people travel by providing dynamic, on-demand mobility that can supplement public transit and personal-vehicle use. Adoption of on-demand mobility has soared across the United States and abroad, driven by the flexibility and affordability that these services offer, particularly in urban areas where population density and land use patterns facilitate a reliable balance of supply and demand. The growth of app-based...

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

Bayen, Eleonore
Nickels, Shirley
Xiong, Glen
Jacquemot, Julien
Subramaniam, Raghav
Bayen, Alexandre
2021

Background: Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fall-related injuries due to the trauma itself, prolonged immobilization on the floor results in a wide range of comorbidities and may double the risk of death in elderly. Thus, reducing the length of Time On the Ground (TOG) in fallers seems crucial in vulnerable individuals with cognitive disorders who cannot get up independently.
Objective: This study aimed to examine the effect of a new technology called SafelyYou Guardian (SYG) on early...

Fuel Consumption Reduction of Multi-Lane Road Networks using Decentralized Mixed-Autonomy Control

Lichtle, Nathan
Vinitsky, Eugene
Gunter, George
Velu, Akash
Bayen, Alexandre M.
2021

In this work, we demonstrate optimization of fuel economy in a large, calibrated model of a portion of the Ventura Freeway using a low penetration rate of controlled autonomous vehicles. We create waves in this network using a string-unstable car-following model and introduce a ghost cell to allow waves to propagate out of the network. Using multi-agent reinforcement learning, we then design a controller that manages to partially dampen the waves and thus increase the average energy efficiency of the system, yielding a 25% fuel consumption reduction at a 10% penetration rate. Finally, we...

Boundary Controllability and Asymptotic Stabilization of a Nonlocal Traffic Flow Model

Bayen, Alexandre
Coron, Jean-Michel
De Nitti, Nicola
Keimer, Alexander
Pflug, Lukas
2021

We study the exact boundary controllability of a class of nonlocal conservation laws modeling traffic flow. The velocity of the macroscopic dynamics depends on a weighted average of the traffic density ahead and the averaging kernel is of exponential type. Under specific assumptions, we show that the boundary controls can be used to steer the system towards a target final state or out-flux. The regularizing effect of the nonlocal term, which leads to the uniqueness of weak solutions, enables us to prove that the exact controllability is equivalent to the existence of weak solutions to the...

The I-24 Trajectory Dataset

Nice, Matthew
Lichtle, Nathan
Gumm, Gracie
Roman, Michael
Vinitsky, Eugene
Elmadani, Safwan
Bayen, Alexandre
2021

This dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes values for Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS and more. This empirical dataset is useful for understanding/simulating real vehicle trajectories and vehicle controller performance.

Longitudinal Deep Truck: Deep Learning and Deep Reinforcement Learning for Modeling and Control of Longitudinal Dynamics of Heavy Duty Trucks

Albeaik, Saleh
Wu, Trevor
Vurimi, Ganeshnikhil
Lu, Xiao-Yun
Bayen, Alexandre
2021

Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of expertise to carry through. This article investigates deep learning and deep reinforcement learning as truck-configuration-agnostic longitudinal modeling and control approaches for heavy duty trucks. The article outlines a process to develop and validate such models and...