Autonomous Vehicles’ Turning Motion Planning for Conflict Areas at Mixed-Flow Intersections

Zhou, Donghao; Ma, Zian; Sun, Jian

doi:10.1109/tiv.2019.2955854

Cited by 35 publications

(11 citation statements)

References 38 publications

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“…Furthermore, two types of motion planning methods: intended cooperation motion planning (ICMP) and a potential field (PF)‐based approach proposed in [46] were installed in the AVs and their behaviours were compared for the same scenarios. The PF‐based approach was recently published and focused on the same multi‐interaction issues in unprotected turning scenarios.…”

Section: Test Results and Analysismentioning

confidence: 99%

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Zhou

Zhang

et al. 2022

IET Intelligent Trans Sys

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Turning behaviour is one of the most challenging driving manoeuvres that take place at intersections. Autonomous vehicles (AVs) are often overly conservative in these scenarios as they compromised to others’ behaviour to realize behavioural consistency. This paper proposes an intended cooperative motion planning (ICMP) that can actively predict the intentions of interacting participants. This helps achieve socially compliant cooperation and enables each vehicle to converge upon a set of consistent behaviours. The ICMP framework divides the integrated driving process into two related modules: prediction and planning. These modules are integrated using the mechanism of intended cooperation, which first introduces the criteria for a cooperative response and then maps them in the motion planning space. The prediction module applies a Gaussian mixture model to learn human drivers’ behaviour to determine conflict points, which helps to narrow down the solution spaces. In the planning module, paths are represented by quartic Bézier curves and speed is modelled as a polynomial function. Optimizations can then be used that maximize the efficiency and smoothness for path planning, and comfort and efficiency for speed planning. The test results show that ICMP‐based AVs had consistent interactions with other vehicles. Moreover, when compared with a potential field‐based method, the ICMP‐based method had better performance in terms of safety, comfort and efficiency, especially when interacting with multiple oncoming vehicles.

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Section: Test Results and Analysismentioning

confidence: 99%

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Zhou

Zhang

et al. 2022

IET Intelligent Trans Sys

Self Cite

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“…T HE NUMBER of passenger cars and commercial vehicles on urban roads has increased rapidly in the past decade, which inevitably causes congestion and numerous accidents [1], [2], [3], [4], [5], [6]. Intersections are the primary source of congestion in an urban transportation system [7], [8], [9]. Traffic signal control and road infrastructure reconstruction are typical approaches for relieving traffic congestion [10].…”

Section: Introductionmentioning

confidence: 99%

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Cao

Tang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Replacing traffic signals with roadside vehicle-toinfrastructure systems in the era of connected and autonomous vehicles (CAVs) is promising. Managing CAVs in a signal-free intersection, known as autonomous intersection management (AIM), controls the driving behavior of each intersection-traverse CAV to maximize the throughput. Although AIM improves the gross throughput, the fairness of each individual vehicle in its right of way is not seriously considered. This study sets up an AIM system in the cyber-physical-social space to trade traverse priorities quantitatively and fairly. To that end, one needs an AIM method that is optimal and stable, otherwise no convincing trades of traverse priorities could be made. This study proposes a near-optimal lane-free AIM method based on numerical optimal control, wherein log-exp functions are deployed to convexify nondifferentiable collision-avoidance constraints. Besides that, a parameterized social force model (SFM) is proposed to provide a tunable initial guess for numerical optimal control. By tuning the urgency weights in SFM, one may get cooperative trajectories in different homotopy classes, which are further utilized to decide the amount of virtual currency to reward those CAVs who tend to share their traverse priorities. The overall method improves the traverse throughput with individual fairness Manuscript

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“…In [9], a multi-point decision making strategy is proposed for human-like driving. To address the interaction and decision making between AV and human-driven vehicle at mixedflow intersections, a logit model is combined with Bayes' theorem to realize human-like interaction and autonomous driving [10]. In [11], an adaptive tactical behavior planner (ATBP) is designed for AVs, which can plan human-like motion behaviors at a unsignalized roundabout.…”

Section: Introductionmentioning

confidence: 99%

Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

Hang¹,

Zhang²,

Lv³

2022

Preprint

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In this paper, a human-like driving framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model, is designed. In the human-like lanechange decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artificial potential field model is built for collision risk assessment. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments on a driving simulator, and the results demonstrated the feasibility and effectiveness of the proposed method.

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Autonomous Vehicles’ Turning Motion Planning for Conflict Areas at Mixed-Flow Intersections

Cited by 35 publications

References 38 publications

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Sharing Traffic Priorities via Cyber–Physical–Social Intelligence: A Lane-Free Autonomous Intersection Management Method in Metaverse

Brain-Inspired Modelling and Decision-making for Human-Like Autonomous Driving in Mixed Traffic Environment

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