This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving and implements it on an embedded system. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. Firstly, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding applied on the grabbed road-scene images. Then the extracted bright objects are processed by to identify the vehicles by locating and analyzing their vehicle light patterns and to estimate their distances to the camera-assisted car by a rule-based procedure. Finally, we also implement the above vision-based techniques on a real-time system mounted in the host car. The proposed vision-based techniques are integrated and implemented on an ARM-Linux embedded platform, as well as the peripheral devices, including image grabbing devices, voice reporting module, and other in-vehicle control devices, will be also integrated to accomplish an in-vehicle embedded vision-based nighttime driver assistance system.