Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic

Li, Xiang; Sun, Jian‐Qiao

doi:10.1007/978-3-319-64063-1_1

Cited by 2 publications

(2 citation statements)

References 64 publications

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“…We investigated differences in human-human and humanautonomous interactions using a full-scale fixed base driving. In our cover story, we mentioned that the AVs were defensively programmed in an interaction and drove conservatively to avoid collisions and had faster braking reaction times than HVs as this is the expected programming of AVs (Zhan et al, 2016;Li and Sun, 2018). However, both, AVs and HVs were simulated according to the same driving behavior.…”

Section: Discussionmentioning

confidence: 99%

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Unni

Trende

Pauley

et al. 2022

Front. Neuroergonomics

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Some studies provide evidence that humans could actively exploit the alleged technological advantages of autonomous vehicles (AVs). This implies that humans may tend to interact differently with AVs as compared to human driven vehicles (HVs) with the knowledge that AVs are programmed to be risk-averse. Hence, it is important to investigate how humans interact with AVs in complex traffic situations. Here, we investigated whether participants would value interactions with AVs differently compared to HVs, and if these differences can be characterized on the behavioral and brain-level. We presented participants with a cover story while recording whole-head brain activity using fNIRS that they were driving under time pressure through urban traffic in the presence of other HVs and AVs. Moreover, the AVs were programmed defensively to avoid collisions and had faster braking reaction times than HVs. Participants would receive a monetary reward if they managed to finish the driving block within a given time-limit without risky driving maneuvers. During the drive, participants were repeatedly confronted with left-lane turning situations at unsignalized intersections. They had to stop and find a gap to turn in front of an oncoming stream of vehicles consisting of HVs and AVs. While the behavioral results did not show any significant difference between the safety margin used during the turning maneuvers with respect to AVs or HVs, participants tended to be more certain in their decision-making process while turning in front of AVs as reflected by the smaller variance in the gap size acceptance as compared to HVs. Importantly, using a multivariate logistic regression approach, we were able to predict whether the participants decided to turn in front of HVs or AVs from whole-head fNIRS in the decision-making phase for every participant (mean accuracy = 67.2%, SD = 5%). Channel-wise univariate fNIRS analysis revealed increased brain activation differences for turning in front of AVs compared to HVs in brain areas that represent the valuation of actions taken during decision-making. The insights provided here may be useful for the development of control systems to assess interactions in future mixed traffic environments involving AVs and HVs.

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Section: Discussionmentioning

confidence: 99%

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Unni

Trende

Pauley

et al. 2022

Front. Neuroergonomics

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“…This directly results in the efficiency of computation which is directly reflected in the efficiency of the fleet. Taking inspiration from [14] and [15] in which authors use multi-objective optimization for controlling autonomous vehicles in two ITS scenarios, we formulate the cooperative lane change task as a non-linear optimization problem reducible to a linear-optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Algorithm for Lane Sorting of Autonomous Vehicles

2020

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In this paper, we propose a generalized algorithm that converts traffic composed of vehicles located randomly in a set of lanes into sorted traffic in which vehicles are moved into the lane corresponding to their destination group. Focus is placed on the cooperative behavior of vehicles. The proposed algorithm architecture divides the entire scenario into various independent sections (called frames) that can be processed in parallel at the same time. Processing each frame involves solving an optimization procedure of a nonlinear programming problem reducible to a linear programming problem. The performance of the proposed algorithm is tested using the Simulation of Urban MObility (SUMO) simulator. Results are obtained and presented for average sorting distance required for sorting all vehicles in the scenario for different traffic settings. INDEX TERMS Intelligent transportation system, lane sorting, linear programming, lane assignment, vehicle platooning.

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Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic

Cited by 2 publications

References 64 publications

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

A Cooperative Algorithm for Lane Sorting of Autonomous Vehicles

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