An Adaptive Sequencing Approach for Object Detection in Autonomous Vehicles

被引:0
|
作者
Wolfe, Christopher [1 ]
Mohammadi, Khatereh [1 ]
Ferdowsi, Hasan [1 ]
机构
[1] Northern Illinois Univ, Elect Engn Dept, De Kalb, IL 60115 USA
基金
英国科研创新办公室;
关键词
Autonomous vehicle; Object detection; Aggregate Channel feature; Computational resources;
D O I
10.1007/s13177-024-00401-8
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The integration of autonomous vehicles into our daily lives is steadily advancing, with researchers and institutions contributing to the realization of fully autonomous commercially available vehicles. Among the critical components of such vehicles is object detection, which draws from various fields like image processing and statistics. This article introduces a novel real-time adaptive object detection method inspired by the principles of real-time computing and control systems, meticulously tailored for use in automated vehicle control systems. In this context, it is essential to recognize that computational resources are limited, and this limitation has a direct impact on the reaction time. To address this challenge, our method leverages the aggregate channel features (ACF) detection algorithm, thoughtfully incorporating considerations of computational resources by integrating feedback from the vehicle motion planner. The proposed model undergoes comprehensive analysis and simulation within the MATLAB and Simulink environment, and the results are indeed promising, showcasing significant enhancements in the reaction time.
引用
收藏
页码:629 / 647
页数:19
相关论文
共 50 条
  • [41] A fast lane and vehicle detection approach for autonomous vehicles
    Wu, BF
    Lin, CT
    Chen, CJ
    Lai, TC
    Liao, HL
    Wu, A
    SEVENTH IASTED INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING, 2005, : 305 - 310
  • [42] An object-unified approach to develop controllers for autonomous underwater vehicles
    Soriano, T.
    Hien, N. V.
    Tuan, K. M.
    Anh, T. V.
    MECHATRONICS, 2016, 35 : 54 - 70
  • [43] An Object Classification Approach for Autonomous Vehicles Using Machine Learning Techniques
    Alqarqaz, Majd
    Bani Younes, Maram
    Qaddoura, Raneem
    WORLD ELECTRIC VEHICLE JOURNAL, 2023, 14 (02):
  • [44] Object Scene Flow for Autonomous Vehicles
    Menze, Moritz
    Geiger, Andreas
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 3061 - 3070
  • [45] As autonomous vehicles approach
    McPherson, Allen
    Dzepina, Branislav
    Quinn, Aidan
    Turcotte, Joshua Eric
    SCIENCE, 2018, 359 (6377) : 755 - 755
  • [46] Artificial intelligence based object detection and traffic prediction by autonomous vehicles - A review
    Preeti
    Rana, Chhavi
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
  • [47] A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles
    Meng, Qiao
    Song, Huansheng
    Li, Gang
    Zhang, Yu'an
    Zhang, Xiangqing
    COMPLEXITY, 2019, 2019
  • [48] A Study on Data Selection for Object Detection in Various Lighting Conditions for Autonomous Vehicles
    Lin, Hao
    Parsi, Ashkan
    Mullins, Darragh
    Horgan, Jonathan
    Ward, Enda
    Eising, Ciaran
    Denny, Patrick
    Deegan, Brian
    Glavin, Martin
    Jones, Edward
    JOURNAL OF IMAGING, 2024, 10 (07)
  • [49] Neural Pruning Search for Real-Time Object Detection of Autonomous Vehicles
    Zhao, Pu
    Yuan, Geng
    Cai, Yuxuan
    Niu, Wei
    Liu, Qi
    Wen, Wujie
    Ren, Bin
    Wang, Yanzhi
    Lin, Xue
    2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC), 2021, : 835 - 840
  • [50] Multi-Object Detection and Tracking, Based on DNN, for Autonomous Vehicles: A Review
    Ravindran, Ratheesh
    Santora, Michael J.
    Jamali, Mohsin M.
    IEEE SENSORS JOURNAL, 2021, 21 (05) : 5668 - 5677