Connected and Automated Vehicles

Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

Almatrudi, Sulaiman
Parvate, Kanaad
Rothchild, Daniel
Vijay, Upadhi
2022

Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and...

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Lichtle, Nathan
Vinitsky, Eugene
Nice, Matthew
Seibold, Benjamin
Work, Dan
Bayen, Alexandre M.
2022

Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I–24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human...

Learning Energy-Efficient Driving Behaviors by Imitating Experts

Rahman Kreidieh, Abdul
Fu, Zhe
Bayen, Alexandre M.
2022

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 heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in...

A Rigorous Multi-Population Multi-Lane Hybrid Traffic Model for Dissipation of Waves via Autonomous Vehicles

Kardous, Nicolas
Hayat, Amaury
McQuade, Sean T.
Gong, Xiaoqian
Truong, Sydney
Bayen, Alexandre
2022

In this paper, a multi-lane multi-population microscopic model, which presents stop-and-go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the traffic instabilities, and in particular stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on...

A Rigorous Multi-Population Multi-Lane Hybrid Traffic Model for Dissipation of Waves via Autonomous Vehicles

Kardous, Nicolas
Hayat, Amaury
McQuade, Sean T.
Gong, Xiaoqian
Truong, Sydney
Bayen, Alexandre M.
2022

In this paper, a multi-lane multi-population microscopic model, which presents stop-and-go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the traffic instabilities, and in particular stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on...

Learning Energy-Efficient Driving Behaviors by Imitating Experts

Kreidieh, Abdul Rahman
Fu, Zhe
Bayen, Alexandre M.
2022

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 heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in...

On the Approximability of Time Disjoint Walks

Bayen, Alexandre
Goodman, Jesse
Vinitsky, Eugene
2022

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 a destination. The goal is to find a walk and delay for each demand so that no two trips occupy the same vertex at the same time, and so that a min–max or min–sum objective over the trip durations is realized. We focus here on the min–sum variant...

A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming

Wu, Fangyu
Bayen, Alexandre M.
2023

Single-lane car-following is a fundamental task in autonomous driving. A desirable car-following controller should keep a reasonable range of distances to the preceding vehicle and do so as smoothly as possible. To achieve this, numerous control methods have been proposed: some only rely on local sensing; others also make use of non-local downstream observations. While local methods are capable of attenuating high-frequency velocity oscillation and are economical to compute, non-local methods can dampen a wider spectrum of oscillatory traffic but incur a larger cost in computing. In this...

Optimizing Mixed Autonomy Traffic Flow with Decentralized Autonomous Vehicles and Multi-Agent Reinforcement Learning

Vinitsky, Eugene
Lichtle, Nathan
Parvate, Kanaad
Bayen, Alexandre
2023

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. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties...

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

Bhadani, Rahul
Bunting, Matthew
Nice, Matthew
Bayen, Alexandre M.
2023

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software- and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed...