Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

Wang, Zhitao; Zheng, Yang; Li, Shengbo Eben; You, Keyou; Li, Keqiang

doi:10.1109/itsc.2018.8569956

Cited by 24 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides deducing the intentions of other human traffic participants, driving behavior of the AV also need to be inferred by other vehicles [4]. Modeling interactions as multi-agent game [14] or cooperative optimization [15], [16], which will generate behavior more interactive rather than reactive. However, both modeling the game and dealing with the complexity of solution are all intriguing problems.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

Wang¹,

Zhuang²,

Gu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

For autonomous vehicles integrating onto roadways with human traffic participants, it requires understanding and adapting to the participants' intention and driving styles by responding in predictable ways without explicit communication. This paper proposes a reinforcement learning based negotiation-aware motion planning framework, which adopts RL to adjust the driving style of the planner by dynamically modifying the prediction horizon length of the motion planner in real time adaptively w.r.t the event of a change in environment, typically triggered by traffic participants' switch of intents with different driving styles. The framework models the interaction between the autonomous vehicle and other traffic participants as a Markov Decision Process. A temporal sequence of occupancy grid maps are taken as inputs for RL module to embed an implicit intention reasoning. Curriculum learning is employed to enhance the training efficiency and the robustness of the algorithm. We applied our method to narrow lane navigation in both simulation and real world to demonstrate that the proposed method outperforms the common alternative due to its advantage in alleviating the social dilemma problem with proper negotiation skills.

show abstract

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

Wang¹,

Zhuang²,

Gu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ADMM algorithm for the optimization herein is shown in Algorithm 1. It includes use of the primal residual 2-norm r and dual residual 2-norm s to both evaluate convergence and adjust ρ from a default value during iterations in an effort to keep r and s within a factor of 10 (Factor of 10 chosen through numerical experimentation of the problem herein and suggestion in [7]) of each other [7,28]. Convergence is achieved when r 2 and s 2 are both less than tolerance values, r and s , respectively.…”

Section: Distributed Controlmentioning

confidence: 99%

Distributed Switched Optimal Control of an Electric Vehicle

Meyer

2020

Energies

View full text Add to dashboard Cite

Distributed control is investigated to solve an electric vehicle switched optimal control problem faster than centralized control without significant performance change. The powertrain includes a cooling system, supercapacitor, and two switched mode components: a battery with discharging and charging modes and an electric drive with motoring and generating modes. Control-oriented component power flow models are developed with mode and temperature dependence. Component specific power and thermal management optimization problems, subject to these models, require solution for overall powertrain management. The power management problem is switched, having discrete-valued mode selection variables. Both problems are solved in a distributed manner using the alternating direction method of multipliers (ADMM). An ADMM-based algorithm to solve the switched powertrain management problem is proposed; it (i) solves the embedded version of the switched problem that relaxes discrete mode switch values to continuous values and (ii) projects embedded mode selection values onto discrete values and then solves the problem with now known mode selections. The distributed solution approach is demonstrated using a trapezoidal drive profile and three regulatory profiles. The regulatory results are compared to centralized control and the proposed algorithm achieved at least a 3.3 times improvement in solution time with comparable drive performance.

show abstract

“…Wang et al. [16] applied the ADMM to solve the centralized optimization problem of cooperative automation of large‐scale connected vehicles in parallel.…”

Section: Introductionmentioning

confidence: 99%

A priority tree based coordination method for intelligent and connected vehicles at unsignalized intersections

Dongxin¹,

Qiqige²,

Chu³

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Intelligent and connected vehicles are believed to be the future solution to traffic management, especially in highly challenging areas such as intersections. In this paper, a priority tree based coordination method is proposed for intelligent and connected vehicles at unsignalized intersections. First, a dynamic scheduling method is used to generate the crossing order of the vehicles, considering the conflicting relationship, waiting time, and arrival time of the vehicles. Then a conflict resolution method is presented to handle the spatial and temporal conflicts among the vehicles inside the intersection. And the simulation results show that the method can generate collision-free traffic flows as well as improving the traffic performance.

show abstract

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

Cited by 24 publications

References 26 publications

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

Distributed Switched Optimal Control of an Electric Vehicle

A priority tree based coordination method for intelligent and connected vehicles at unsignalized intersections

Contact Info

Product

Resources

About