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Pedestrian and Vehicle Recognition Base on Radar for Autonomous Emergency Braking (基於雷達之行人與車輛辨識針對自動緊急煞車系統)
  • 發布年度:2017
  • 主要類別:駕駛輔助
  • 次要類別:論文
  • 自動緊急制動系統通常包含雷達,(立體)攝影機和/或基於LiDAR的技術,以識別車輛前方的潛在碰撞對象,從而警告駕駛員或自動制動以避免或減輕碰撞。然而,毫米波雷達和攝影機在自動緊急制動系統中對於偵測地面的缺陷,包括毫米波雷達在橫向上提供比縱向更低的精度,並且攝影機在短距偵測中(約0-1公尺)提供相對粗略的精度檢測,這會導致的錯誤偵測的結果。為了誤檢檢測的改進,我們提出了一種基於雷達的行人與車輛識別算法(PVR),用於自動緊急制動系統。 PVR使用雷達橫截面反射強度(RCS)和障礙物寬度的標準偏差來確定行人及車輛的寬度的標準偏差是否越過RCS的閾值,並且分別識別目標是行人或車輛。最後,根據實驗數據結果驗證了所提出的演算法達到所要求的辨識效能。

    Abstract
    Autonomous Emergency Braking Systems (AEBS) usually contain radar, (stereo) camera and/or LiDAR-based technology to identify potential collision partners ahead of the car, such that to warn the driver or automatically brake to avoid or mitigate a crash. The advantage of camera is less cost: however, is inevitable to face the defects of cameras in AEBS, that is, the image recognition cannot perform good accuracy in the poor or over-exposure light condition. Therefore, the compensation of other sensors is of importance. Motivated by the improvement of false detection, we propose a Pedestrian-and-Vehicle Recognition (PVR) algorithm based on radar to apply to AEBS. The PVR employs the radar cross section (RCS) and standard deviation of width of obstacle to determine whether a threshold value of RCS and standard deviation of width of the pedestrian and vehicle is crossed, and to identity that the objective is a pedestrian or vehicle, respectively. The performance of the proposed algorithm is pressed via the experimental test data.