This paper presents a low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors (accelerometers and gyroscopes), global navigation satellite system (GNSS), and commercially available road network maps. The system can be used for intelligent transportation systems, telematics applications, and autonomous driving. The system does not depend on visual markings or highly precise GNSS technology, such as DGPS or RTK, and it does not need explicit lane-level resolution maps. High-resolution estimation of the vehicle's position, velocity, and orientation is implemented by fusing inertial sensors with GNSS in a loosely coupled mode using extended Kalman filter. A curve-to-curve road-level map-matching is implemented using a hidden Markov model followed by a least-square regression step that estimates the vehicle's lane. The system includes a lane-change detector based on inertial sensors and the filtered vehicle's state. The system has been realized in real time and tested extensively on real-road data. Experiments showed robust map-matching in challenging road intersections and a 97.14% lane-determination success rate.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Low-Cost Lane-Determination System Using GNSS/IMU Fusion and HMM-Based Multistage Map Matching




    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    A Low-Cost Lane-Determination System Using GNSS/IMU Fusion and HMM-Based Multistage Map Matching

    Atia, Mohamed Maher / Hilal, Allaa R. / Stellings, Clive et al. | IEEE | 2017


    Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU

    Takanose, Aoki / Kitsukawa, Yuki / Megruo, Junichi et al. | IEEE | 2021


    GNSS/INS-based vehicle lane-change estimation using IMM and lane-level road map

    Jiang Liu, / Baigen Cai, / Wang, Jian et al. | IEEE | 2013


    GNSS Based Lane Keeping Assist System via Model Predictive Control

    Kitano, Hiroaki / Berntorp, Karl / Tominaga, Kenta et al. | SAE Technical Papers | 2019


    GNSS Photo Matching: Positioning using GNSS and Camera in Urban Canyon

    Suzuki, Taro / Kubo, Nobuaki | British Library Conference Proceedings | 2015