Analysis of Lane Changing Behavior of Passenger Cars on the Freeway Using UAVs

MA Xiao-long1 YU Qiang1 LIU Jian-bei2,3 MA Yuan-yuan2,3

(1.School of Automobile, Chang’an University, Xi’an, Shaanxi Province, China 710064)
(2.CCCC First Highway Consultants Co., Ltd., Xi’an, Shaanxi Province, China 710075)
(3.Research and Development Center on Emergency Support Technologies for Transport Safety, Xi’an, Shaanxi Province, China 710075)

【Abstract】Two UAVs (Unmanned Aerial Vehicle) were used to simultaneously shoot videos at a height of 200 m to study the discretionary lane changing behavior of small passenger cars on the freeway. Also, a high-resolution map was generated. The two videos were spliced and the accurate running states of vehicles at each frame were obtained, including eight key indicators, such as lane, speed, and ID of the vehicle. A total of 1 520 pieces of lane changing behavior were extracted and 942 discretionary lane changing behaviors were sorted out. The starting and ending points of lane changing behavior were determined based on whether the trajectory of the vehicle continued the offset. On this basis, sixteen characteristic parameters, such as the duration, distance, mutual state with surrounding vehicles, and safety of lane changing behavior, were analyzed. It is concluded that the average lane changing duration is 6.09 s and the average of lane changing distance is 148.08 m. A lognormal distribution provides the best fitting to the lane changing duration and distance. The average distance between the lane changing vehicle and the following vehicle in the target lane is found to be the shortest (34.29 m). The relative distance within 10 m accounts for 28.24%. Even though at a short relative distance between the lane changing vehicle and the following vehicle in the target lane, drivers make the lane changing decision for higher speed. The relative speed difference between the lane changing vehicle and the leading vehicle in the original lane is the greatest, with a mean of 10.2 km·h−1. Moreover, in 83% of cases, the lane changing vehicle is traveling faster than the leading vehicle in the original lane. It is fully explained that the discretionary lane change of vehicles is caused by the slow speed of the leading vehicles. TTC (time to collision) and MTC (margin to collision) are used to analyze the safety state at the beginning of lane change. The safety states can be classified into four types: severe–emergency, severe–non-emergency, non-severe–emergency, non-severe–non-emergency. Of them, severe–non-emergency and non-severe–non-emergency states account for the highest proportions. The results of this research can help understand the lane changing characteristics of drivers on freeway in China. The research results also have certain reference values for the establishment of a lane changing model suitable for the actual traffic environment in China.

【Keywords】 traffic engineering; lane changing characteristics; statistical analysis; lane changing behavior; UAV and high-resolution map; freeway;

【DOI】

【Funds】 National Key R&D Program of China (2017YFC0803904, 2017YFC0803900)

Download this article

    References

    [1] XU Bing, LIU Xiao, WANG Zi-yang, et al. Fusion Decision Model for Vehicle Lane Change With Gradient Boosting Decision Tree [J]. Journal of Zhejiang University (Engineering Science), 2019 (06): 1–10 (in Chinese).

    [2] OH C, CHOI J, PARK S. In-depth Understanding of Lane Changing Interactions for In-Vehicle Driving Assistance Systems [J]. International Journal of Automotive Technology, 2017, 18 (2): 357–363.

    [3] CAO P, HU Y, MIWA T, et al. An Optimal Mandatory Lane Change Decision Model for Autonomous Vehicles in Urban Arterials [J]. Journal of Intelligent Transportation Systems, 2017.

    [4] WANG Xue-song, YANG Min-min. Cut-in Behavior Analyses Based on Naturalistic Driving Data [J]. Journal of Tongji University (Natural Science), 2018, 46 (08): 1057–1063 (in Chinese).

    [5] Wang X, Yang M, Hurwitz D. Analysis of Cut-in Behavior Based on Naturalistic Driving Data [J]. Accident Analysis and Prevention, 2019, 124: 127–137.

    [6] WANG Chang, SONG Ding-bo, FU Rui, et al. Identification of Lane Change Safety Characteristic of Large Commercial Bus on Expressway [J]. China Journal of Highway and Transport, 2018, 31 (09): 229–238 (in Chinese).

    [7] Wang Q, Li Z, Li L. Investigation of Discretionary Lane-Change Characteristics Using Next-Generation Simulation Data Sets [J]. Journal of Intelligent Transportation Systems, 2014, 18 (3): 246–253.

    [8] ZHANG Ying-da, SHAO Chun-fu, LI Hui-xua, et al. Microscopic Characteristics of Lane-Change Maneuvers Based on NGSIM [J]. Journal of Transport Information and Safety. 2015, 33 (06): 19–24 (in Chinese).

    [9] OLSEN E C B, LEE S E, WIERWILLE W W, et al. Analysis of Distribution, Frequency, and Duration of Naturalistic Lane Changes [J]. Human Factors & Ergonomics Society Annual Meeting Proceedings, 2002, 46 (22): 1789–1793.

    [10] LI Li, HUANG Xiao-meng GUO Yan-jun, et al. Study on Time of Line Crossing Prediction Method for Operating Passenger [J]. China Safety Science Journal, 2016, 26 (04): 90–95 (in Chinese).

    [11] Wang Chang. Research on Several Key Problems of Vehicle Lane Change Warning [D]. Chang’an University, 2012 (in Chinese).

    [12] WOO H, YONGHOON J I, KONO H, et al. Lane-Change Detection Based on Vehicle-Trajectory Prediction [J]. IEEE Robotics & Automation Letters, 2017, 2 (2): 1109–1116.

    [13] ZHOU B, WANG Y, YU G, et al. A Lane-Change Trajectory Model from Drivers’Vision View [J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 609–627.

    [14] GU X, ABDEL-ATY M, XIANG Q, et al. Utilizing UAV Video Data for In-Depth Analysis of Drivers’Crash Risk At Interchange Merging Areas [J]. Accident Analysis & Prevention, 2019, 123: 159–169.

    [15] BALAL E, CHEU R L, GYAN-SARKODIE T, et al. Analysis of Discretionary Lane Changing Parameters on Freeways [J]. International Journal of Transportation Science and Technology, 2014, 3 (3): 277–296.

    [16] CHEN T, SHI X, WONG Y D. Key Feature Selection and Risk Prediction for Lane-Changing Behaviors Based on Vehicles’ Trajectory Data [J]. Accident Analysis & Prevention, 2019, 129: 156–169.

    [17] HAO Zhi-guo. Research on Prediction and Safety Assessment of Expressway Lane Change Conflict [D]. Jilin University, 2019 (in Chinese).

    [18] ANTIN J F, LEE S, PEREZ M A, et al. Second Strategic Highway Research Program Naturalistic Driving Study Methods [J]. Safety Science, 2019, 119: 2–10.

    [19] LI L, LV C, CAO D, et al. Retrieving Common Discretionary Lane Changing Characteristics From Trajectories [J]. IEEE Transactions on Vehicular Technology, 2018, 67 (3): 2014–2024.

    [20] YUAN Wei,XU Yuan-xin, GUO Ying-shi, et al. Fixation transfer characteristics of drivers during lane change and straight drive [J]. Journal of Chang’an University (Natural Science Edition), 2015, 35(5): 124–130 (in Chinese).

    [21] WANG Xue-song, LI Yan. Characteristics Analysis of Lane Changing Behavior Based on the Naturalistic Driving Data [J]. Journal of Transport Information and Safety, 2016, 34 (01): 17–22 (in Chinese).

    [22] Mahmud S M S, Ferreira L, Hoque M S, et al. Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs [J]. IATSS Research, 2017, 41 (4): 153–163.

    [23] Toledo T, Zohar D. Modeling Duration of Lane Changes [J]. Transportation Research Record Journal of the Transportation Research Board, 2007, 1999 (1): 71–78.

    [24] MA Yong, FU Rui, GUO Ying-shi, et al. Multi-parameter prediction of driver’s lane change behavior based on real-world driving tests [J]. Journal of Chang’an University (Natural Science Edition), 2014, 34 (5): 101–108 (in Chinese).

    [25] ZHANG Fa, XUAN Hui-yu, ZHAO Qiao-xia. Lane Changing Model Based on Finite State Automaton [J]. China Journal of Highway and Transport, 2008, 21 (3): 97–100, 111 (in Chinese).

    [26] WANG Chang, FU Rui, ZHANG Qiong, et al. Research on Parameter TTC Characteristics of Lane Change Warning System [J]. China Journal of Highway and Transport, 2015, 28 (08): 91–100 (in Chinese).

    [27] LIU Kai, JIA Jie, LIU Chao, et al. Warming Effectiveness of Vehicle-to-infrastructure Cooperative Crossing Collision Prevention System at Non-signal Controlled Intersection [J]. China Journal of Highway and Transport, 2018, 31 (4): 222–230 (in Chinese).

    [28] Weng J, Xue S, Yang Y, et al. In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas [J]. Accident Analysis and Prevention, 2015, 77: 51–61.

    [29] ZHU Shun-ying, JIANG Ruo-xi, WANG Hong, et al. Review of Research on Traffic Conflict Techniques [J]. China Journal of Highway and Transport, 2020, 33 (02): 15–33 (in Chinese).

This Article

ISSN:1001-7372

CN: 61-1313/U

Vol 33, No. 06, Pages 95-105

June 2020

Downloads:0

Share
Article Outline

Abstract

  • 0 Introduction
  • 1 Data acquisition and processing
  • 2 Starting and ending points of lane change and characteristic parameters
  • 3 Analysis of lane changing characteristics
  • 4 Safety analysis of lane changing vehicles
  • 5 Conclusions
  • References