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Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research. Index Terms--Vehicle detection, computer vision, intelligent vehicles.

Ultrathin core-shell V 3 S 4 @C nanosheets assembled into hierarchical nanotubes (V 3 S 4 @C NS-HNTs) are synthesized by a self-template strategy and evaluated as general anodes for alkali-ion batteries. Structural/physicochemical characterizations and DFT calculations bring insights into the intrinsic relationship between crystal structures and electrochemical mechanisms of the V 3 S 4 @C NS-HNTs electrode. The V 3 S 4 @C NS-HNTs are endowed with strong structural rigidness owing to the layered VS 2 subunits and interlayer occupied V atoms, and efficient alkali-ion adsorption/diffusion thanks to the electroactive V 3 S 4 -C interfaces. The resulting V 3 S 4 @C NS-HNTs anode exhibit distinct alkali-ion-dependent charge storage mechanisms and exceptional long-durability cyclic performance in storage of K + , benefiting from synergistic contributions of pseudocapacitive and reversible intercalation/de-intercalation behaviors superior to those of the conversion-reaction-based Li + -/Na + -storage counterparts., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.