Automated Multi-Object Tracking for Autonomous Vehicle Control in Dynamically Changing Traffic

Hunde, Andinet; Ayalew, Beshah; Wang, Qian

doi:10.23919/acc.2019.8814447

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Furthermore, the prevalent assumption about track initiation is that our knowledge of the prior information about the tentative target velocity and acceleration vectors are limited to the maximum speed

and the maximum acceleration

[ 29 , 30 ]. In this approach, the initial state estimate and covariance matrix formulations follow a similar structure as presented in our previous works [ 31 , 32 , 33 ]. Here, the presence of other groups in the FoV of the ego vehicle can be used to improve our prior information on the velocity and acceleration of a tentative track.…”

Section: Multiple Detection and Extended Object (Group) Trackingmentioning

confidence: 99%

Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections

Hunde

2022

Sensors

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This paper discusses the perception and tracking of individual as well as group targets as applied to multi-lane public traffic. Target tracking problem is formulated as a two hierarchical layer problem—on the first layer, a multi-target tracking problem based on multiple detections is distinguished in the measurement space, and on the second (top) layer, group target tracking with birth and death as well as merging and splitting of group target tracks as they evolve in a dynamic scene is represented. This configuration enhances the multi-target tracking performance in situations including but not limited to target initialization(birth), target occlusion, missed detections, unresolved measurement, target maneuver, etc. In addition, group tracking exposes complex individual target interactions to help in situation assessment which is challenging to capture otherwise.

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and the maximum acceleration

Section: Multiple Detection and Extended Object (Group) Trackingmentioning

confidence: 99%

Multi-Target State and Extent Estimation for High Resolution Automotive Sensor Detections

Hunde

2022

Sensors

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“…A longitudinal IMPT method in an MPC framework is proposed in [102], in which a robust invariant terminal set is computed in real time to ensure the safety and can handle the road grade and the motion prediction uncertainties of the front vehicle. An MPC-based framework incorporating a multi-target tracking method and a motion planning method is proposed in [103], which can handle unreliable detections and uncertain changes of surrounding vehicles. An IMPT method for maximizing lateral clearance against a construction zone is proposed in [104], in which a bias penalty function of the construction area is added in the total cost function.…”

Section: Mpc For Combined Motion Planning and Control Of An Individual Agvmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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