Connected and Automated Vehicles

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...

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...

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...

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,...

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Lichtle, Nathan
Vinitsky, Eugene
Nice, Matthew
Bhadani, Rahul
Bunting, Matthew
2024

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient...

Car-Following Models: A Multidisciplinary Review

Zhang, Tianya Terry
Jin, Peter J.
McQuade, Sean T.
Bayen, Alexandre
Piccoli, Benedetto
2024

Car-following (CF) algorithms are crucial components of traffic simulations and have been integrated into many production vehicles equipped with Advanced Driving Assistance Systems (ADAS). Insights from the model of car-following behavior help researchers to understand the causes of various macro phenomena that arise from interactions between pairs of vehicles. Car-following Models encompass multiple disciplines, including traffic engineering, physics, dynamic system control, cognitive science, machine learning, deep learning, and reinforcement learning. This paper presents an extensive...

Reduce Emissions and Improve Traffic Flow Through Collaborative Autonomy

Patire, Anthony D.
Dion, Francois
Bayen, Alexandre M.
2024

This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow...