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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Two-dimensional active sensing system for bicyclist-motorist crash prediction
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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- Award ID(s):
- 1631133
- NSF-PAR ID:
- 10038725
- Date Published:
- Journal Name:
- American Control Conference (ACC), 2017
- Page Range / eLocation ID:
- 2315 to 2320
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Vehicle‐to‐Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non‐line‐of‐sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors' failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, a Context‐Aware Target Classification (CA‐TC) module coupled with a hybrid learning‐based predictive modeling technique for CVS systems is proposed. The CA‐TC consists of two modules: a Context‐Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA‐TC, making them more robust and reliable. The CAM leverages vehicles' path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real‐world data, a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior is learned. Offline training and online model updates are combined with on‐the‐fly forecasting to account for new possible driver behaviors. Finally, the framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC.2017.7963298
