Situation assessment and decision making for lane change assistance using ensemble learning methods

Hou, Yi; Edara, Praveen; Sun, Carlos

doi:10.1016/j.eswa.2015.01.029

Cited by 115 publications

(48 citation statements)

References 35 publications

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“…Lee, Choi, Yi, Shin, & Ko, 2014) and lane change assistance (see e.g. Hou, Edara, & Sun, 2015;Luo, Xiang, Cao, & Li, 2016), longitudinal speed assistance (see e.g. Martinez & Canudas-deWit, 2007), and intersection assistance (see e.g.…”

Section: Literature Resultsmentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

706

383

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KEYWORDSautomated driving; first, second, and third order impacts; policy and societal implications; ripple effect ABSTRACTIn this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

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Section: Literature Resultsmentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

706

383

View full text Add to dashboard Cite

show abstract

“…Intelligent driving technologies are designed for the purpose of facilitating driving strategies that improve driving safety and reduce driver work load. Examples include, but are not limited to the: Lane-Change Decision Aid System (LCDAS), Adaptive Cruise Control (ACC), and Lane Departure Warning (LDW) [1][2][3][4]. Changing lanes is one of the most dangerous driving maneuvers, and accounts for about 5% of traffic collisions in China and Europe [5,6].…”

Section: Introductionmentioning

confidence: 99%

Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Wang

Sun

et al. 2020

Sensors

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Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver's risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver's perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle's Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s 2 and 1.76 m/s 2 , respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle's driver's safety.interval from 3 m to 25 m, from 25 m to 45 m, and from 45 m to 70 m. Wakasugi [20] recommended a two-level TTC threshold of 3 s and 5 s, respectively. However, the computed TTC threshold would be easily influenced by the relative speed and distance between the rear vehicle and the subject vehicle.The remainder of the paper is organized as follows. Related works on lane-change decision models and safety indicator thresholds are introduced in Section 2. Section 3 introduces the naturalistic lane-change trial and extreme moment trials. Section 4 presents the lane-change decision model and the calibration parameters of the model. The two-level threshold based on the proposed model is determined in Section 5. Finally, a discussion and conclusion are presented in Section 6. MethodOn-road experiment is the main research method used in this paper, and the experiments include the naturalistic lane-change trial and extreme moment trials. The purpose of the naturalistic trial is to calibrate the parameters of the proposed lane-change decision model. The extreme moment trials are used to accurately capture the variation of driver cognition characteristics of lane-change safety. In this section, we willintroduce the required equipment, participants, test route and procedures for the experiments in detail. ApparatusThe test vehicle is depicted in Figure 1. The test vehicle used in our experiments was a 2008 Volkswagen Touran, equipped with a Lane Mark Recognition system (Mobileye C2-170, made by Mobileye Company, Jerusalem, Isra...

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“…However, the existing studies cannot reflect the implicit difference in driving behaviour in the real traffic flow [20]. Due to the importance of driver's lane changing intention and behaviour, many scholars conduct studies about recognizing lane changing intention [21], and apply the drivers' behavioural preferences [22], cooperation intention [23,24] and sensitivity factor [25,26] to design the lane changing model. In these studies, the driver characteristics are introduced into the lane changing model.…”

Section: Introductionmentioning

confidence: 99%

Multi-lane Changing Model with Coupling Driving Intention and Inclination

Chang

Zhu

Yan

2017

PROMET

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Situation assessment and decision making for lane change assistance using ensemble learning methods

Cited by 115 publications

References 35 publications

Policy and society related implications of automated driving: A review of literature and directions for future research

Policy and society related implications of automated driving: A review of literature and directions for future research

Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Multi-lane Changing Model with Coupling Driving Intention and Inclination

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