Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to improve the overall detection capabilities. Additionally, robust motion tracking is essential for reducing the effect of sensor noise and improving state estimation accuracy. The reliability of the autonomous vehicle software becomes even more relevant in complex, adversarial high-speed scenarios at the vehicle handling limits in autonomous racing. In this paper, we present a modular multi-modal sensor fusion and tracking method for high-speed applications. The method is based on the Extended Kalman Filter (EKF) and is capable of fusing heterogeneous detection inputs to track surrounding objects consistently. A novel delay compensation approach enables to reduce the influence of the perception software latency and to output an updated object list. It is the first fusion and tracking method validated in high-speed real-world scenarios at the Indy Autonomous Challenge 2021 and the Autonomous Challenge at CES (AC@CES) 2022, proving its robustness and computational efficiency on embedded systems. It does not require any labeled data and achieves position tracking residuals below 0.1 m.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing


    Contributors:

    Published in:

    Publication date :

    2023-07-01


    Size :

    1478881 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

    Lombaerts, Thomas / Shish, Kimberlee H. / Keller, Gordon et al. | TIBKAT | 2022



    Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

    Lombaerts, Thomas / Shish, Kimberlee H. / Keller, Gordon et al. | TIBKAT | 2022


    Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

    Thomas Lombaerts / Kimberlee H. Shish / Gordon Keller et al. | NTRS