Human Drivers Based Active-Passive Model for Automated Lane Change

Huy, Quoc; Tehrani, Hossein; Mita, Seiichi; Egawa, Masumi; Muto, Kenji; Yoneda, Keisuke

doi:10.1109/mits.2016.2613913

Cited by 80 publications

(32 citation statements)

References 28 publications

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“…is important to determine whether the side vehicle is located in front of or behind the ego vehicle. Also, the relative velocity between the ego vehicle and the side vehicle needs to be considered in lane change situations [41]. A larger distance is required when the velocity of the rear vehicle is faster than that of the front vehicle.…”

Section: B Safe Distancementioning

confidence: 99%

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Chae

2020

IEEE Access

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This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle. INDEX TERMS Autonomous vehicles, autonomous driving, decision-making, motion planning, vehicle control, overtaking, lane change, virtual target.

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Section: B Safe Distancementioning

confidence: 99%

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Chae

2020

IEEE Access

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“…The decisions represent how the driver evaluates a defined set of conditions related to a specific macro scenario and performs a given course of action [15]. A macro scenario refers to the global situation being analysed, like for example, the moment in which the driver activates the left turn signal to let the other drivers know that he intends to perform the overtake in the coming seconds.…”

Section: Parameter Selectionmentioning

confidence: 99%

Autonomous Driving Scenario Generation in Overtake Manoeuvres Through Data Fusion

Pinheiro

Stanke

Ferraris

et al. 2020

Mechanisms and Machine Science

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For an effective study of specific driving scenarios, in particular related to overtaking manoeuvres, developing well-thought-out manoeuvre databases from the acquired data will greatly improve the analysis process. The key point being that identifying clearly the studied scenario and clustering the manoeuvres based on specific sub-cases of this will bring an extra dimension of information that allows to visualise existing correlations between a given set of conditions and the manoeuvres performed under them. This paper expands on how to obtain these databases starting from only from overtaken manoeuvres extracted from experimental data. Consisting in two main procedures, the manoeuvre classification itself, in which a hybrid-classifier that combines both supervised and unsupervised algorithms generates the scenario sub-cases, and the database compilation stage, in where it displays the different types of databases that can be created, based on the aim of the study.

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“…In [5], the authors simulated a lane merge scenario and applied pattern recognition for decision-making. The pattern model consists of a nine grid cell, in which each cell is marked as blocked or unblocked according to the information of surrounding vehicles.…”

Section: State Of the Artmentioning

confidence: 99%

A Lane Merge Coordination Model for a V2X Scenario

Sequeira

Szefer

Slome

et al. 2019

2019 European Conference on Networks and Communications (EuCNC)

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Cooperative driving using connectivity services has been a promising avenue for autonomous vehicles, with the low latency and further reliability support provided by 5 th Generation Mobile Network (5G). In this paper, we present an application for lane merge coordination based on a centralised system, for connected cars. This application delivers trajectory recommendations to the connected vehicles on the road. The application comprises of a Traffic Orchestrator as the main component. We apply machine learning and data analysis to predict whether a connected vehicle can successfully complete the cooperative manoeuvre of a lane merge. Furthermore, the acceleration and heading parameters that are necessary for the completion of a safe merge are elaborated. The results demonstrate the performance of several existing algorithms and how their main parameters were selected to avoid overfitting. Index Terms-Lane merge, intelligent transport system, V2X communications, edge cloud, machine learning.

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Human Drivers Based Active-Passive Model for Automated Lane Change

Cited by 80 publications

References 28 publications

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

Autonomous Driving Scenario Generation in Overtake Manoeuvres Through Data Fusion

A Lane Merge Coordination Model for a V2X Scenario

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