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Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera.