Distributed Conflict Resolution for Connected Autonomous Vehicles

Liu, Changliu; Lin, Chung-Wei; Shiraishi, Shinichi; Tomizuka, Masayoshi

doi:10.1109/tiv.2017.2788209

Cited by 109 publications

(63 citation statements)

References 17 publications

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“…If the intent of all vehicles is known, the global solution is known to be NP-hard and quickly becomes intractable with large numbers of vehicles. Thus, many approaches look to find locally-optimal solutions, using control policies that guarantee safe passage [2], [3], [4], [5], [6], game theoretic approaches [7], learning-based control methods [8], [9], and decentralized algorithms [10], [11]. In this paper, we use a central coordinator to manage human and autonomous vehicles using intersection reservations.…”

Section: A Related Workmentioning

confidence: 99%

Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Buckman

Pierson

Schwarting

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Current methods for autonomous management use strict first-come, first-serve (FCFS) ordering to manage incoming autonomous vehicles at an intersection. In this work, we present a coordination policy that swaps agent ordering to increase the system-wide performance while ensuring that the swaps are socially compliant. By considering an agent's Social Value Orientation (SVO), a social psychology metric for their willingness to help another vehicle, the central coordinator can reduce system delays while ensuring each individual vehicle increases their own utility. The FCFS-SVO algorithm is both computationally tractable and accounts for a variety of realworld agent types, such as human drivers and a variety of social orientations. Simulation results show that average vehicle delays decrease with swapping by enabling cooperation between agents. In addition, we show that the proportion of human drivers, as well as, the distribution of prosocial and egoistic vehicles in the system can have a prominent effect on the performance of the system.

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Section: A Related Workmentioning

confidence: 99%

Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Buckman

Pierson

Schwarting

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…1 The classification step is needed when the communications among vehicles are limited. Otherwise, vehicles can broadcast their planned behaviors as discussed in [88].…”

Section: Cognition: Understanding Other Road Participantsmentioning

confidence: 99%

“…For example, if vehicles can directly exchange information about their intended behaviors, the difficulty of predicting other vehicles' trajectories (as discussed in Chapter 3 and Chapter 8) would be greatly reduced. A method to incorporate the communicated information into motion planning is discussed in [88]. In the future, the effect of direct communication among agents will be considered.…”

Section: • To Account For Diverse Modes Of Interactionsmentioning

confidence: 99%

Designing Robot Behavior in Human-Robot Interactions

Liu¹,

Tian²,

Lin³

et al. 2019

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Designing Robot Behavior in Human-Robot InteractionsHuman-robot interactions (HRI) have been recognized to be a key element of future robots in many application domains such as manufacturing, transportation, service and entertainment. These applications entail huge social and economical impacts. Future robots are envisioned to function as human's counterparts, which are independent entities that make decisions for themselves; intelligent actuators that interact with the physical world; and involved observers that have rich senses and critical judgements. Most importantly, they are entitled social attributions to build relationships with humans. We call these robots corobots.Technically, it is challenging to design the behavior of co-robots. Unlike traditional robots that work in structured and deterministic environments, co-robots need to operate in highly unstructured and stochastic environments. The fundamental research question to address in this dissertation is how to ensure that co-robots operate efficiently and safely in dynamic uncertain environments.The focus of this dissertation is 1) to set up a unified analytical framework for various human-robot systems; 2) to establish a methodology to design the robot behavior to address the fundamental problem.A multi-agent framework to model human-robot systems is introduced in Chapter 2. In order to address the uncertainties during human-robot interactions, a unique parallel planning and control architecture is introduced in Chapter 2, which has a cognition module for human behavior estimation and human motion prediction, a long term global planner to ensure efficiency of robot behavior, and a short term local planner to ensure real time safety under uncertainties. The functionalities of these components are discussed in Chapter 3 to Chapter 5. Chapter 3 discusses the cognition module, which includes offline classification and online adaptation of various human behaviors. Chapter 4 and Chapter 5 discuss the optimal control or optimization problems for short term and long term robot motion planning. In a cluttered environment, the optimization problems are highly nonlinear and non-convex, hence hard to solve in real time, which may delay the robot's response in emergency situations. Fast online algorithms are developed to handle the issue: the convex feasible setThe past five years has been an incredible journey in my life. I would not have come this far without the tremendous help from many people.My deep and sincere gratitude goes to my Ph.D. advisor Professor Masayoshi Tomizuka, who has been a great mentor to me during the past five years for his profound knowledge, insightful visions, enthusiasm on work, and humor in life. Moreover, Professor Tomizuka respects all my research ideas, inspires me to explore the unknowns, and offers the strongest support in my career development. I sincerely wish I could be a great person, an extraordinary mentor as he is in the future.

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“…The statistical mean delay under each scenario is obtained through the time-driven traffic simulation for 10 min. The details of the simulation is provided in an earlier journal [11]. The expected steady state delay is computed using the result from Proposition 1.…”

Section: B Case 2: Lane Merging With Fomentioning

confidence: 99%

Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions

Liu

Kochenderfer

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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With the emergence of autonomous vehicles, it is important to understand their impact on the transportation system. However, conventional traffic simulations are timeconsuming. This paper introduces an analytical traffic model for unmanaged intersections accounting for microscopic vehicle interactions. The macroscopic property, i.e., delay at the intersection, is modeled as an event-driven stochastic dynamic process, whose dynamics encode the microscopic vehicle behaviors. The distribution of macroscopic properties can be obtained through either direct analysis or event-driven simulation. They are more efficient than conventional (time-driven) traffic simulation, and capture more microscopic details compared to conventional macroscopic flow models. We illustrate the efficiency of this method by delay analyses under two different policies at a twolane intersection. The proposed model allows for 1) efficient and effective comparison among different policies, 2) policy optimization, 3) traffic prediction, and 4) system optimization (e.g., infrastructure and protocol).

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Distributed Conflict Resolution for Connected Autonomous Vehicles

Cited by 109 publications

References 17 publications

Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Designing Robot Behavior in Human-Robot Interactions

Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions

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