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This paper develops an active sensing system for a bicycle to accurately track rear vehicles that can have two-dimensional motion. The active sensing system consists of a single-beam laser sensor mounted on a rotationally controlled platform. The sensing system is inexpensive, small, lightweight, consumes low power, and is thus ideally suited for the bicycle application. The rotational orientation of the laser sensor needs to be actively controlled in real-time in order to continue to focus on a rear vehicle, as the vehicle’s lateral and longitudinal distances change. This tracking problem requires controlling the real-time angular position of the laser sensor without knowing the future trajectory of the vehicle. The challenge is addressed using a novel receding horizon framework for active control and an interacting multiple model framework for estimation. The features and benefits of this active sensing system are illustrated first using simulation results. Then, preliminary experimental results are presented using an instrumented bicycle to show the feasibility of the system in tracking rear vehicles during both straight and turning maneuvers.
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On-Bicycle Vehicle Tracking at Traffic Intersections Using Inexpensive Low-Density Lidar
This paper explores the challenges in developing an inexpensive on-bicycle sensing system to track vehicles at a traffic intersection. In particular, opposing traffic with vehicles that can travel straight or turn left are considered. The estimated vehicle trajectories can be used for collision prevention between bicycles and left-turning vehicles. A compact solid-state 2-D low-density Lidar is mounted at the front of a bicycle to obtain distance measurements from vehicles. Vehicle tracking can be achieved by clustering based approaches for assigning measurement points to individual vehicles, introducing a correction term for position measurement refinement, and by exploiting data association and interacting multiple model Kalman filtering approaches for multi-target tracking. The tracking performance of the developed system is evaluated by both simulation and experimental results. Two types of scenarios that involve straight driving and left turning vehicles are considered. Experimental results show that the developed system can successfully track cars in these scenarios accurately in spite of the low measurement density of the sensor.
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- Award ID(s):
- 1631133
- PAR ID:
- 10198604
- Date Published:
- Journal Name:
- 2019 American Control Conference
- Page Range / eLocation ID:
- 593 to 598
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC.2019.8814442
