Abstract
In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD) filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art.
Highlights
Traffic accidents are a major cause of death worldwide
This paper extends our previous work to detect vehicles by using information from LiDAR, where objects are represented by the position and shape parameters
In the vehicle detection scenario, a large number of measurements would be collected from the surface of a single object, called “Extended Target (ET)
Summary
Traffic accidents are a major cause of death worldwide. A study by the World Health Organization (WHO) reports that an estimated 1.2 million people die in traffic accidents every year, and up to 50 million people are injured [1]. Numerous ADAS functions have been developed to help drivers avoid accidents, improve driving efficiency, and reduce driver fatigue, in which vehicle detection plays an important role. To avoid the data association issue, the Probability Hypothesis Density (PHD). Sensors 2016, 16, 510 several approaches are developed to avoid the data association issue, including the PHD filter, the Cardinalized PHD (CPHD) filter [17] and the Bernoulli filter [18]. Unlike the PHD or CPHD filter, the multi-Bernoulli filter propagates the posterior target density It has the same complexity as the PHD filter, the performance is better in highly nonlinear environments (it does not require the additional clustering step for state estimation) [18].
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