Nighttime Vehicle Detection for Intelligent Headlight Control

Antonio Lopez, Joerg Hilgenstock, Andreas Busse, Ramon Baldrich, Felipe Lumbreras, Joan Serrat

Research output: Contribution to journalArticleResearchpeer-review

52 Citations (Scopus)


A good visibility of the road ahead is a major issue for safe nighttime driving. However, high beams are sparsely used because drivers are afraid of dazzling others. Thus, the intelligent automatic control of vehicles' headlight is of great relevance. It requires the detection of oncoming and preceding vehicles LIP to such a distance that only camera based approaches are reliable. At nighttime, detecting vehicles using a camera requires to identify their head or tail lights. The main challenge of this approach is to distinguish these lights from reflections due to infrastructure elements. In this paper we confront such a challenge by using a novel image sensor also suitable for other driver assistance applications. Different appearance features obtained from that sensor are used as input to a novel classifier-based module which, for each detected target, yields a degree of resemblance to a vehicle light. This resemblance is integrated in time using a novel temporal coherence analysis which allows to react in one single frame for targets that; are clear vehicle lights, or in only a few frames for those whose type is more difficult to discern.
Original languageEnglish
Pages (from-to)113-124
Number of pages2
JournalLecture Notes in Computer Science (LNCS)
Publication statusPublished - 2008


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