Comparative Analysis of Following Distances in Different Adaptive Cruise Control Systems at Steady Speeds

被引:1
作者
Mohammed, Dilshad [1 ,2 ]
Horvath, Balazs [2 ]
机构
[1] Univ Duhok, Dept Civil Engn, Duhok 1006, Iraq
[2] Szecheny Istvan Univ, Dept Transport, H-9026 Gyor, Hungary
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2024年 / 15卷 / 03期
基金
英国科研创新办公室;
关键词
autonomous vehicles; adaptive cruise control; average clearance; constant driving speed; sensors; TRAFFIC-FLOW; IMPACT; RADAR; SAFETY;
D O I
10.3390/wevj15030116
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds.
引用
收藏
页数:20
相关论文
共 53 条
  • [1] Adaptive cruise control radar-based positioning in GNSS challenging environment
    Abosekeen, Ashraf
    Karamat, Tashfeen B.
    Noureldin, Aboelmagd
    Korenberg, Michael J.
    [J]. IET RADAR SONAR AND NAVIGATION, 2019, 13 (10) : 1666 - 1677
  • [2] ACC radar sensor technology, test requirements, and test solutions
    Abou-Jaoude, R
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2003, 4 (03) : 115 - 122
  • [3] Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors
    Arnold, Eduardo
    Dianati, Mehrdad
    de Temple, Robert
    Fallah, Saber
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (03) : 1852 - 1864
  • [4] PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
    Bai, Zhengwei
    Wu, Guoyuan
    Barth, Matthew J.
    Liu, Yongkang
    Sisbot, Emrah Akin
    Oguchi, Kentaro
    [J]. 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2022, : 1743 - 1749
  • [5] Bishop R, 2005, ANN TELECOMMUN, V60, P228
  • [6] Boehlau C., 2009, P SAE WORLD C EXHIBI, DOI [10.4271/2009-01-0639, DOI 10.4271/2009-01-0639]
  • [7] Will Automated Vehicles Negatively Impact Traffic Flow?
    Calvert, S. C.
    Schakel, W. J.
    van Lint, J. W. C.
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2017,
  • [8] Evaluation and Optimization of Responsibility-Sensitive Safety Models on Autonomous Car-Following Maneuvers
    Chai, Chen
    Zeng, Xianming
    Wu, Xiangbin
    Wang, Xuesong
    [J]. TRANSPORTATION RESEARCH RECORD, 2020, 2674 (11) : 662 - 673
  • [9] Moran-Moguel MC, 2018, J IMMUNOL RES, V2018, DOI [10.1155/2018/2474529, 10.1155/2018/6135183]
  • [10] Car-Following Characteristics of Adaptive Cruise Control from Empirical Data
    Goodall, Noah J.
    Lan, Chien-Lun
    [J]. JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS, 2020, 146 (09)