Lane Change Decision Analysis Based on Drivers' Perception-Judgment and Game Theory

Peng, Jin Shuan; Guo, Ying Shi; Shao, Yi Ming

doi:10.4028/www.scientific.net/amm.361-363.1875

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…In addition, factors such as continuous driving time, age, and driving seniority can affect the driver's speed and distance perception [22]. The researchers also suggested that even veteran drivers tend to underestimate their driving speed while commuting [23,24].…”

Section: Introductionmentioning

confidence: 99%

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Zhao

et al. 2022

Sustainability

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This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.

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Section: Introductionmentioning

confidence: 99%

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Zhao

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…For humandriven vehicles, Kita first built a mixed strategy game model for vehicles on the highway according to the time to collision [3]. On this basis, the risk conflicts [4], safety and efficiency of lane changing [5][6][7], driving space [8], and lane-changing requirements [9] were considered in the mixed strategy lanechanging game models on the highway or urban streets. Kang and Rakha [10] proposed a repeated game model by calculating the accumulated payoffs for the players so as to explore the impacts of the history of the players' decisionmaking on the choice of strategies.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Dynamics of Mandatory Lane Changing for Bus Exiting

Yao

et al. 2021

Journal of Advanced Transportation

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With the development of the connected autonomous bus, the interactions between the bus and social vehicle during the mandatory lane changing for bus exiting become more diverse and complex. This research investigates the evolutionary dynamics of behavioral decision-making for the bus and social vehicle in different scenarios. The evolutionary game model for the connected autonomous bus and social vehicle is built, as do the human-driven bus and social vehicle, and the connected autonomous bus under different penetration rates and social vehicle. The results of numerical experiments reveal that the connected autonomous bus chooses to change lanes in most instances, and the strategies of the human-driven bus show conservative tendencies. Such tendencies are weakened when the connected autonomous bus and human-driven bus are mixed. As for the social vehicle in different scenarios, the strategies that balance overall traffic safety and efficiency are promoted. This research provides some references for intelligent decision-making of lane changing in urban public transportation.

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“…Lane change warning systems (LCW) are gradually being developed and popularized in vehicle active safety systems [11,12]. By monitoring the surrounding vehicles and issuing collision warnings to the driver in advance, the system reduces the risk of potential conflict, improves land change safety, and reduces the driver's operating load [13][14][15]. According to the collected road and traffic information, current LCWs generally utilize the safety lane change evaluation algorithm to determine whether the driver will encounter danger during the lane change process.…”

Section: Introductionmentioning

confidence: 99%

Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study

Zhang

Wang

et al. 2020

Sensors

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Speed has an important impact on driving safety, however, this factor is not included in existing safety warning algorithms. This study uses lane change systems to study the influence of vehicle speed on safety warning algorithms, aiming to determine lane change warning rules for different speeds (DS-LCW). Thirty-five drivers are recruited to carry out an extreme trial and naturalistic driving experiment. The vehicle speed, relative speed, relative distance, and minimum safety deceleration (MSD) related to lane change characteristics are then analyzed and calculated as warning rule characterization parameters. Lane change warning rules for a rear vehicle in the target lane under four-speed levels of 60 ≤ v < 70 km/h, 70 ≤ v < 80 km/h, 80 ≤ v < 90 km/h, and v ≥ 90 km/h are established. The accuracy of lane change warning rules not considering speed level (NDS-LCW) and ISO 17387 are found to be 87.5% and 79.8%, respectively. Comparatively, the accuracy rate of DS-LCW under four-speed levels is 94.6%, 93.8%, 90.0%, and 92.6%, respectively, which is significantly superior. The algorithm proposed in this paper provides warning in the lane change process with a smaller relative distance, and the accuracy rate of DS-LCW is significantly superior to NDS-LCW and ISO 17387.

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Lane Change Decision Analysis Based on Drivers' Perception-Judgment and Game Theory

Cited by 12 publications

References 7 publications

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

Evolutionary Dynamics of Mandatory Lane Changing for Bus Exiting

Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study

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