嵌入式影像輔助駕駛系統－前方及盲點車輛辨識演算法之研究

A Vision-Based Front Side and Blind-Spot Car Detection Algorithm for an Embedded Driving Assistant System

馮威(Wei Feng)

指導教授 ： 曾百由

國立臺北科技大學/機電學院/機電整合研究所/碩士(2010年)

摘要

一般的影像系統都是建立在高運算執行速度的電腦平台上，然因成本、耗電量特性及體積上的侷限，本論文採用高速嵌入式DSP處理器開發智慧型車輛影像辨識系統。本文所開發的影像系統功能分為道路標線辨識、前車辨識及盲點車輛辨識，這些資訊在智慧型車輛系統中可以輔助駕駛做出行車安全的判斷，例如車道偏離警示、前車碰撞警示、輔助變換車道、前車跟隨及主動式巡航等。本文使用的嵌入式平台搭配所開發的演算法可以達到每一個畫面運算處理僅需25ms左右的處理時間，足以即時(real-time)辨識車道線位置以及前車特徵和盲點區域車輛辨識，完成一個可實際應用於智慧型車輛之嵌入式影像系統。

關鍵字

嵌入式系統 ； 前車辨識 ； 盲點車輛辨 ； 識空間特徵演算法

並列摘要

Image processing systems are normally built on the high performance computer platform, however, the system cost is subjected to the size and power consumption.
Therefore, this research focuses mainly on the development of a low cost intelligent vehicle detection driving assistant system using high-speed embedded DSP image processor. The functions of this vision-based system include Lane Sign Detection (LSD) system, Front Car Detection (FCD) function, and Blind Spot Detection (BSD). With the aid of the developed techniques, this intelligent image processing system can provide immediate assistance and accurate driving information to the drivers, such as Lane Departure Warning (LDW), Front Collision Warning (FCW), Lane Change Assistant (LCA), and Adaptive Cruise Control (ACC).
The real-time capability of this developed embedded and system algorithm requires only 25ms per frame to perform the lane mark tracking and front car positioning. The blind spot car detection can be perform individually will in 7 ms. Thus the development of this research can be deployed in the commercial embedded vision-based driving assistant system and future research of intelligent vehicle system.

並列關鍵字

Embedded system ； front car detection ； blind spot detection ； knowledge-based algorithm

參考文獻

[7] G. P. Stein, E. Rushinek, G. Hayun, and A. Shashua, “A Computer Vision System on a Chip: A Case Study from the Automotive Domain,” Proceedings of the 2005 IEEE Computer Society Conference, Jun, 2005, pp. 130.

[8] R. Sosa, and G. Velazquez, “Obstacles Detection and Collision Avoidance system Developed with Virtual Models,” ICVES, IEEE International Conference on ITESM, Dec. 2007.

[9] Seongkeun Park, JaePil Hwanga, Euntai Kim, and Hyung-Jin Kang, “Vehicle Tracking Using a Microwave Radar for Situation Awareness,” Control Engineering Practice, vol. 18, Issue 4, April, 2010, pp. 383-395.

[11] R. Labayrade, C. Royere, and D. Aubert, “A Collision Mitigation System Using Laser Scanner and Stereovision Fusion and its Assessment,” Procs. IEEE Intelligent Vehicles Symposium, Las Vegas, USA, June, 2005, pp. 441–446.

[12] T. Emura, and M. Kumagai, “A Non-scanning Ultrasonic Sensor for Driver Assistant Systems,” IEEE Intelligent Vehicle Symposium, June, 2002, pp. 98-103.