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Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration.

Mekala, M.S.; Dhiman, Gaurav; Viriyasitavat, Wattana; Park, Ju H.; Jung, Ho-Youl


Gaurav Dhiman

Wattana Viriyasitavat

Ju H. Park

Ho-Youl Jung


Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches.


MEKALA, M.S., DHIMAN, G., VIRIYASITAVAT, W., PARK, J.H. and JUNG, H.-Y. 2024. Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration. IEEE transactions on intelligent transportation systems [online], 25(3), pages 2708-2718. Available from:

Journal Article Type Article
Acceptance Date Jan 23, 2023
Online Publication Date Feb 16, 2023
Publication Date Mar 31, 2024
Deposit Date Feb 20, 2023
Publicly Available Date Feb 20, 2023
Journal IEEE transactions on intelligent transportation systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 25
Issue 3
Pages 2708-2718
Keywords Mobile computing; BDAI model; Box object filter method; Deep learning; Multi-object tracking system
Public URL


MEKALA 2024 Efficient LiDAR-trajectory (AAM) (29.3 Mb)

Copyright Statement
© 2023 IEEE.

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