Alexandre M. Bayen

Alexandre M. Bayen
University of California, Berkeley | UCB · Department of Electrical Engineering and Computer Sciences, Department of Civil and Environmental Engineering

Ph.D.

About

348
Publications
66,898
Reads
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11,212
Citations
Introduction
Alexandre Bayen received the Engineering Degree in applied mathematics from the Ecole Polytechnique, France, in July 1998, the M.S. degree in aeronautics and astronautics from Stanford University in June 1999, and the Ph.D. in aeronautics and astronautics from Stanford University in December 2003.. He has been an Assistant Professor in the Department of Civil and Environmental Engineering at UC Berkeley since January 2005, and an Associate Professor since 2010.
Additional affiliations
March 2015 - present
University of California, Berkeley
Position
  • Chancellor Professor
March 2015 - present
University of California, Berkeley
Position
  • Chancellor Professor
February 2015 - present
Lawrence Berkeley National Laboratory
Position
  • Faculty Scientist, Mechanical Engineering

Publications

Publications (348)
Preprint
Full-text available
Emerging vehicular systems with increasing proportions of automated components present opportunities for optimal control to mitigate congestion and increase efficiency. There has been a recent interest in applying deep reinforcement learning (DRL) to these nonlinear dynamical systems for the automatic design of effective control strategies. Despite...
Preprint
Full-text available
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely h...
Conference Paper
Full-text available
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely h...
Article
Building control systems for heavy duty trucks have historically been dependent on availability of the details of the mechanical configuration of each target truck. This article investigates transfer and robustness of continuous control systems learned using model free deep-RL as an alternative; a configuration agnostic strategy for control system...
Article
This study focuses on the comprehensive investigation of stop-and-go waves appearing in closed-circuit ring road traffic wherein we evaluate various longitudinal dynamical models for vehicles. It is known that the behavior of human-driven vehicles, with other traffic elements such as density held constant, could stimulate stop-and-go waves, which d...
Chapter
A vehicle with different (eventually controlled) dynamics from general traffic along a street may reduce the road capacity, thus generating a moving bottleneck , and can be used to act on the traffic flow. The interaction between the controlled vehicle and the surrounding traffic, and the consequent flow reduction at the bottleneck position, can be...
Chapter
This chapter focuses on control of systems of conservation laws with boundary data. Problems with one or two boundaries are considered and, in particular, we focus on cases where shocks may be developed by the solution. However, for completeness we briefly discuss in Sect. 2.2 other existing results where singularities are prevented via suitable fe...
Chapter
Conservation and/or balance laws on networks in the recent years have been the subject of intense study, since a wide range of different applications in real life can be covered by such a research.
Chapter
In this chapter, we introduce Hamilton-Jacobi PDEs. These PDEs are related to conservation laws and their solutions are the anti-derivative (in space) of the Entropy solutions of the corresponding conservation law, given that some assumptions are satisfied.
Chapter
This chapter focuses on control of systems of conservation laws with distributed parameters. Problem with different parameterized fluxes is addressed: in particular, we deal with cases where the control is the maximal speed and look for continuous dependence of the solution on parameters.
Preprint
Full-text available
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 ana...
Preprint
Full-text available
Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, has been a challenge for 1) systems with low-latency requirements, 2) systems with limited computational resources, and 3) systems with fast and complex dynamics. To address...
Preprint
Full-text available
Reinforcement learning techniques can provide substantial insights into the desired behaviors of future autonomous driving systems. By optimizing for societal metrics of traffic such as increased throughput and reduced energy consumption, such methods can derive maneuvers that, if adopted by even a small portion of vehicles, may significantly impro...
Preprint
Full-text available
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows - applied in macroscopic and mesoscopic traffic modeling - the article introduces a new N-player dynamic routing game with explicit conges...
Article
We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governed by a macroscopic Aw-Rascle-Zhang (ARZ) model, consisting of 2 x 2 quasi-linear partial differential equations (PDEs) for traffic density and velocity. The boundary stab...
Preprint
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. Thi...
Article
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partia...
Article
Full-text available
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 Ti...
Article
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 syste...
Preprint
Full-text available
Traffic assignment is one of the key approaches used to model the congestion patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time dynamics, it is not designed to represent the complex dynamics of transportation networks as usage changes throughout the day. Dynamic traffic assignment methods...
Preprint
Full-text available
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 execute...
Preprint
Full-text available
This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of...
Conference Paper
Full-text available
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games. While there has been significant innovation in MARL algorithms, algorithms tend to be tested and tuned on a single domain and their average performance across multiple domains is less characterized. Furthermore, since...
Preprint
Full-text available
Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent problems. In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value function. Using a 1-GPU desktop, we show that MAPP...
Preprint
Accurate and reliable prediction of traffic measurements plays a crucial role in the development of modern intelligent transportation systems. Due to more complex road geometries and the presence of signal control, arterial traffic prediction is a level above freeway traffic prediction. Many existing studies on arterial traffic prediction only cons...
Preprint
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of tasks or environments in which a policy will be trained. However, creating a useful distribution of environments is error prone, and takes a significant amount of developer t...
Preprint
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco-Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Alt...
Conference Paper
Full-text available
In this article, we demonstrate a zero-shot transfer of an autonomous driving policy from simulation to University of Delaware's scaled smart city with adversarial multi-agent reinforcement learning, in which an adversary attempts to decrease the net reward by perturbing both the inputs and outputs of the autonomous vehicles during training. We tra...
Preprint
One of the most critical components of an urban transportation system is the coordination of intersections in arterial networks. With the advent of data-driven approaches for traffic control systems, deep reinforcement learning (RL) has gained significant traction in traffic control research. Proposed deep RL solutions to traffic control are design...
Preprint
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed. The Robust RL formulation tackles this by adding worst-case adversarial noise to the dynamics and constructing the noise distribution as the solution to a zero...
Article
Full-text available
Introduction: Informal caregiving is an essential element of health-care delivery. Little data describes how caregivers structure care recipients' lives and impact their functional status. Methods: We performed observational studies of community dwelling persons with dementia (PWD) to measure functional status by simultaneous assessment of physi...
Article
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...
Preprint
When a neural network is partitioned and distributed across physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus on resiliency of training in distributed neural networks. However, resiliency of inference in distribut...
Article
Full-text available
We introduce the combinatorial optimization problem Time Disjoint Walks (TDW), which has applications in collision-free routing of discrete objects (e.g., autonomous vehicles) over a network. This problem takes as input a digraph \(G\) with positive integer arc lengths, and \(k\) pairs of vertices that each represent a trip demand from a source to...
Preprint
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 Ti...
Conference Paper
Full-text available
This article applies the existing Markovian traffic assignment framework to novel traffic control strategies. In the Markovian traffic assignment framework, transition matrices are used to derive the traffic flow allocation. In contrast to the static traffic assignment, the framework only requires flow split ratio at every intersection, bypassing t...
Preprint
Full-text available
Accurate runoff forecasting is crucial for reservoir operators as it allows optimized water management, flood control and hydropower generation. Land surface models in mountainous regions depend on climatic inputs such as precipitation, temperature and solar radiation to model the water and energy dynamics and produce runoff as output. With the rap...
Preprint
Full-text available
This article presents a novel algorithm for promoting cooperation between internal actors in a goal-conditioned hierarchical reinforcement learning (HRL) policy. Current techniques for HRL policy optimization treat the higher and lower level policies as separate entities which are trained to maximize different objective functions , rendering the HR...
Preprint
Full-text available
In this article, we demonstrate a zero-shot transfer of an autonomous driving policy directly from an autonomous driving simulator to the University of Delaware Scaled Smart City under stochastic disturbances. Using adversarial multi-agent reinforcement learning, in which an adversary attempts to decrease the net reward by perturbing both the input...
Article
F all injury is the leading cause of Alzheimer dementia-related hospitalizations, with $5.3 billion in annual cost to Medicare. 1,2 In residential care facilities, individuals with dementia fall, on average, 4 times per year and twice as often as other residents. 3 Falls often go unwitnessed. Due to uncertainty regarding the seriousness of each fal...
Preprint
We propose an integrated control architecture to address the gap that currently exists for efficient real-time implementation of MPC-based control approaches for highly nonlinear systems with fast dynamics and a large number of control constraints. The proposed architecture contains two types of controllers: base controllers that are tuned or optim...
Article
The impact of the recent increase in routing apps usage on road traffic remains uncertain to this day. The article introduces, for the first time, a criterion to evaluate a distance between an observed state of traffic and the user equilibrium of the traffic assignment: the average marginal regret. The average marginal regret provides a quantitativ...
Preprint
This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang (ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to...
Article
This paper presents the problem of block simplex constrained signal recovery, which has been demonstrated to be a suitable formulation for estimation problems in networks such as route flow estimation in traffic. There are several natural approaches to this problem: compressed sensing, Bayesian inference, and convex optimization. This paper present...
Preprint
This article develops a Reinforcement Learning (RL) boundary controller of stop-and-go traffic congestion on a freeway segment. The traffic dynamics are governed by a macroscopic Aw-Rascle-Zhang (ARZ) model, consisting of $2\times 2$ nonlinear Partial Differential Equations (PDEs) for traffic density and velocity. The boundary actuation of traffic...
Preprint
This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to...
Conference Paper
Full-text available
Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with noise injecte...
Article
This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to...
Preprint
Full-text available
Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement learning in micro-simulators, we train two policies, one policy with noise injected into the state and action space and one without any injected noise. In simulation, the...
Article
Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must there...
Conference Paper
This article is focused on measuring the impact of navigational apps on road traffic patterns. We first define the marginal regret, which characterizes the difference between the travel time experienced on the most optimal path and the path of interest between the same origin destination pair. We then introduce a new metric, the average marginal re...