Connected and Automated Vehicles

Motion Planning in Understructured Road Environments with Stacked Reservation Grids

Wu, Fangyu
Wang, Dequan
Hwang, Minjune
Hao, Chenhui
Alexandre Bayen
2008

Motion planning of autonomous vehicles in understructured road environments is challenging owing to a lack of an efficient and analyzable representation of the contextual information. On one hand, idealistic representations like splines, while efficient and interpretable, are not versatile enough to encode the full complexity of the environment. On the other hand, high-fidelity representations like images, although rich in contents, are computationally expensive to decode and are not readily analyzable. To address this problem, we propose a new data structure named stacked reservation grid (...

Autonomous River Navigation Using the Hamilton–Jacobi Framework for Underactuated Vehicles

Weekly, Kevin
Tinka, Andrew
Anderson, Leah
Alexandre Bayen
2014

The feasibility of drifter studies in complex and tidally forced water networks has been greatly expanded by the introduction of motorized floating sensors. This paper presents a method for such motorized sensors to accomplish obstacle avoidance and path selection using the solutions to Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations. The method is then validated experimentally.

Future Road Transportation Technology

Wang, Junhua
Iwasaki, Randy
Alexandre Bayen
Harvey, John
2016

As road transportation brings us great life changes, it also brings safety and environment problems. Near one million people died in road accidents each year and road constructions consumed un-countable ...

ZUbers against ZLyfts Apocalypse: An Analysis Framework for DoS Attacks on Mobility-As-A-Service Systems

Yuan, Chenyang
Thai, Jérôme
Alexandre Bayen
2016

The vulnerability of Mobility-as-a-Service (MaaS) systems to Denial-of-Service (DoS) attacks is studied. We use a queuing-theoretical framework to model the re-dispatch process used by operators to maintain a high service availability, as well as potential cyber-attacks on this process. It encompasses a customer arrival rate model at different sections of an urban area to pick up vehicles traveling within the network. Expanding this re-balance model, we analyze DoS cyber-attacks of MaaS systems by controlling a fraction of the cars maliciously through fake reservations (so called Zombies)...

Emergent Behaviors in Mixed-Autonomy Traffic

Wu, Cathy
Kreidieh, Aboudy
Vinitsky, Eugene
Alexandre Bayen
2017

Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of automation on transportation networks. The present article formulates and approaches the mixed-autonomy traffic control problem (where both automated and human-driven vehicles are present) using the powerful framework of deep reinforcement learning (RL). The resulting...

Stabilizing Traffic with Autonomous Vehicles

Wu, Cathy
Alexandre Bayen
Mehta, Ankur
2018

Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis,...

Benchmarks for Reinforcement Learning in Mixed-Autonomy Traffic

Vinitsky, Eugene
Kreidieh, Aboudy
Flem, Luc Le
Kheterpal, Nishant
Jang, Kathy
Alexandre Bayen
2018

We release new benchmarks in the use of deep reinforcement learning (RL) to create controllers for mixed-autonomy traffic, where connected and autonomous vehicles (CAVs) interact with human drivers and infrastructure. Benchmarks, such as Mujoco or the Arcade Learning Environment, have spurred new research by enabling researchers to effectively compare their results so that they can focus on algorithmic improvements and control techniques rather than system design. To promote similar advances in traffic control via RL, we propose four benchmarks, based on three new traffic scenarios,...

Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion

Vinitsky, Eugene
Parvate, Kanaad
Kreidieh, Aboudy
Wu, Cathy
Alexandre Bayen
2018

Using deep reinforcement learning, we derive novel control policies for autonomous vehicles to improve the throughput of a bottleneck modeled after the San Francisco-Oakland Bay Bridge. Using Flow, a new library for applying deep reinforcement learning to traffic micro-simulators, we consider the problem of improving the throughput of a traffic benchmark: a two-stage bottleneck where four lanes reduce to two and then reduce to one. We first characterize the inflow-outflow curve of this bottleneck without any control. We introduce an inflow of autonomous vehicles with the intent of...

Dissipating Stop-and-Go Waves in Closed and Open Networks via Deep Reinforcement Learning

Kreidieh, Abdul Rahman
Wu, Cathy
Alexandre Bayen
2018

This article demonstrates the ability for model-free reinforcement learning (RL) techniques to generate traffic control strategies for connected and automated vehicles (CAVs) in various network geometries. This method is demonstrated to achieve near complete wave dissipation in a straight open road network with only 10% CAV penetration, while penetration rates as low as 2.5% are revealed to contribute greatly to reductions in the frequency and magnitude of formed waves. Moreover, a study of controllers generated in closed network scenarios exhibiting otherwise similar densities and...

Backstepping-Based Time-Gap Regulation for Platoons

Chou, Fang-Chieh
Tang, Shu-Xia
Lu, Xiao-Yun
Alexandre Bayen
2019

The time-gap regulation problem for a cascaded system consisting of platooned automated vehicles following a leading non-automated vehicle is investigated in this article. Under the assumption of uniform boundedness of the acceleration of the leading vehicle, a control design scheme is proposed via an extension of integral backstepping control method, where additional terms that counter the impact due to the speed change of the non-automated vehicle are used. Each automated vehicle is actuated by one backstepping controller, demonstrated by a recursive control design procedure based on...