Vehicle detection is critical components of driver assistance system and self-driving system, and considerable different frameworks have been investigated such as radar, laser and camera-based. Among them, camera-based vehicle detection has an obvious advantage over other systems in that it needs lower cost. However, existing camera-based methods are not robust enough under complex driving scenes. In this work, an end-to-end deep CNN framework is proposed to detect vehicles. Specifically, a monocular depth estimation method is designed to transform the RGB appearance information into depth modality information. Then the vehicle detection module takes the RGB and depth image as inputs to improve the detection performance. The whole network can be trained in an end-to-end manner. The proposed framework is evaluated on the public vehicle detection benchmark KITTI to show the effectiveness of the proposed framework.

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