A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation

Le, Viet-Anh; Malikopoulos, Andreas A.

doi:10.48550/arxiv.2203.17106

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…SVO has been found to be capable of imitating human driver behaviour when integrated into automated vehicle (AV) motion controller design. This integration was found helpful when AV interacted with other cars (Geary & Gouk, 2020;Le & Malikopoulos, 2022;Schwarting et al, 2019) and pedestrians (Crosato et al, 2021(Crosato et al, , 2022. This past work all rests on the idea that the SVO of road users involved in interaction has an impact on the interaction outcome, but as far as we are aware this hypothesis has never been tested empirically.…”

Section: Introductionmentioning

confidence: 99%

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Kalantari¹,

Yang²,

García³

et al. 2022

Preprint

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One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle-pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.

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Section: Introductionmentioning

confidence: 99%

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Kalantari¹,

Yang²,

García³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To address the challenge of unknown surrounding agents' cost functions, attempts have been made to learn the parameters of the cost function in real time [32]- [34]. These parameters usually characterize driving style [27], aggressiveness [34], or social value orientation (SVO) [35], [36]. However, because the time duration of traffic agents' interaction is usually short, the amount of data may not be sufficient to guarantee the performance of the online learning.…”

Section: Introductionmentioning

confidence: 99%

“…8. Multi-vehicle intersection-crossing scenarioThe cost function is designed according to (33)-(35), where θ 1 = 1 and θ j ∈ [1, 100] for j = 2, 3, 4, 5, unknown to the ego vehicle. Note that when θ j = 100, the collision avoidance term in(33) is very lightly weighted, indicating a safety-agnostic vehicle.…”

mentioning

confidence: 99%

Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach

Liu¹,

Tseng²,

Filev³

et al. 2022

Preprint

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This paper proposes a novel decision-making framework for autonomous vehicles (AVs), called predictor-corrector potential game (PCPG), composed of a Predictor and a Corrector. To enable human-like reasoning and characterize agent interactions, a receding-horizon multi-player game is formulated. To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed. In the PG Predictor, the agents' cost functions are heuristically predefined. We acknowledge that the behaviors of other traffic agents, e.g., human-driven vehicles and pedestrians, may not necessarily be consistent with the predefined cost functions. To address this issue, a best response based PG Corrector is designed. In the Corrector, the action deviation between the ego vehicle prediction and the surrounding agents' actual behaviors are measured and are fed back to the ego vehicle decision-making, to correct the prediction errors caused by the inaccurate predefined cost functions and to improve the ego vehicle strategies.Distinguished from most existing game-theoretic approaches, this PCPG 1) deals with multi-player games and guarantees the existence of a pure-strategy Nash equilibrium (PSNE), convergence of the PSNE seeking algorithm, and global optimality of the derived PSNE when multiple PSNE exist; 2) is computationally scalable in a multi-agent scenario; 3) guarantees the ego vehicle safety under certain conditions; and 4) approximates the actual PSNE of the system despite the unknown cost functions of others. Comparative studies between the PG, the PCPG, and the control barrier function (CBF) based approaches are conducted in diverse traffic scenarios, including oncoming traffic scenario and multi-vehicle intersection-crossing scenario.

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A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation

Cited by 2 publications

References 25 publications

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach

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