Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Buckman, Noam; Pierson, Alyssa; Schwarting, Wilko; Karaman, Sertaç; Rus, Daniela

doi:10.1109/iros40897.2019.8967997

Cited by 33 publications

(21 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, Xu et al [193] coordinated the safe passing order of the CAVs based on some heuristic strategies as well as Monte Carlo tree search (MCTS) where the leaf node yields the optimal solution. Buckman et al [194] made use of a heuristic centralized coordination based on the revised FCFS algorithm for navigating the vehicle through the intersection safely. In this work, the agents' order was swapped having a social psychology metric that could reduce delay while improving the utility function of every vehicle agent.…”

Section: Safety and Efficiencymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

View full text Add to dashboard Cite

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.INDEX TERMS Vehicular ad-hoc networks (VANETs), intersection management (IM), vehicle-tovehicle (V2V), vehicle-to-infrastructure (V2I), trajectory planning (TP), spatio-temporal (ST), connected autonomous vehicles (CAVs), vulnerable road users (VRUs), connected vehicles (CVs), autonomous vehicle (AVs). II. INTERSECTION MANAGEMENT (IM) FUNDAMENTALS A. ARCHITECTURE ALTERNATIVESASHKAN GHOLAMHOSSEINIAN (Graduate Student Member, IEEE) received the bachelor's degree in computer engineering-software in Iran, the M.Sc. degree in computer network engineering from Halmstad University, Sweden, and the Post master's degree in communications for intelligent transport systems from Eurecom, France. He is currently pursuing the Ph.D. degree in electrical engineering and information technology with the Communication Networks Group, Technische Universität Ilmenau, Germany. His research interests include vehicular networks, intelligent transport systems, computer networks, and wireless and mobile networks and communications. JOCHEN...

show abstract

Section: Safety and Efficiencymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…time, money, energy, is formally studied as that person's Social Value Orientation (SVO) in social psychology [7,24]. This concept can also be employed to quantify an autonomous agent's willingness to act egoistically or altruistically [4]. As demonstrated in Figure 2, the behavior of a human, or similarly an autonomous agent, can lay on anywhere from absolutely egoistic to absolutely altruistic depending on the weight they assign to the utility of others.…”

Section: Problem Statementmentioning

confidence: 99%

Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic

Toghi¹,

Valiente²,

Sadigh³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to coexist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the maneuver planning problem for autonomous vehicles and investigate how a decentralized reward structure can induce altruism in their behavior and incentivize them to account for the interest of other autonomous and human-driven vehicles. This is a challenging problem due to the ambiguity of a human driver's willingness to cooperate with an autonomous vehicle. Thus, in contrast with the existing works which rely on behavior models of human drivers, we take an end-to-end approach and let the autonomous agents to implicitly learn the decision-making process of human drivers only from experience. We introduce a multi-agent variant of the synchronous Advantage Actor-Critic (A2C) algorithm and train agents that coordinate with each other and can affect the behavior of human drivers to improve traffic flow and safety.

show abstract

“…Therefore, FIFO is not guaranteed to be optimal. Buckman et al [1] integrate a driver behavior model [37] within the basic FIFO framework to incorporate human social preference to address some of the above limitations of FIFO. But the model does not estimate the social preferences in real time; instead, it chooses a fixed preference parameter for each agent.…”

Section: Prior Workmentioning

confidence: 99%

“…A turn-based ordering, σ, over a given set A is gametheoretically optimal if it is incentive compatible, welfare maximizing, and can be computed in polynomial time 1 .…”

Section: Definition Iii2 Optimal Turn-based Ordering (σ Opt )mentioning

confidence: 99%

“…Auction-based methods, on the other hand, model non-ideal agents in unsignaled traffic scenarios effectively albeit using a monetary-based bidding strategy that is not realizable in real-world scenarios [3], [34], [24], [39]. Our formulation, 1 These terms are defined in Section IV-B GAMEPLAN, differs in this regard wherein we use a novel online driving behavior-based bidding strategy using the CMetric model [8]. In the rest of this section, we present the main algorithm followed by an analysis of its optimality and provide efficiency guarantees.…”

Section: Our Algorithm: Gameplanmentioning

confidence: 99%

See 1 more Smart Citation

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Chandra¹,

Manocha²

2021

Preprint

View full text Add to dashboard Cite

We present a new method for multi-agent planning involving human drivers and autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging. In multi-agent planning, the main challenge is to predict the actions of other agents, especially human drivers, as their intentions are hidden from other agents. Our algorithm uses game theory to develop a new auction, called GAMEPLAN, that directly determines the optimal action for each agent based on their driving style (which is observable via commonly available sensors like lidars and cameras). GAMEPLAN assigns a higher priority to more aggressive or impatient drivers and a lower priority to more conservative or patient drivers; we theoretically prove that such an approach, although counter-intuitive, is gametheoretically optimal. Our approach successfully prevents collisions and deadlocks. We compare our approach with prior state-of-the-art auction techniques including economic auctions, time-based auctions (first-in first-out), and random bidding and show that each of these methods result in collisions among agents when taking into account driver behavior. We additionally compare with methods based on deep reinforcement learning, deep learning, and game theory and present our benefits over these approaches. Finally, we show that our approach can be implemented in the real-world with human drivers.

show abstract

Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Cited by 33 publications

References 26 publications

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic

GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging

Contact Info

Product

Resources

About