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Vehicle detection in severe weather has always been a difficult task in the environmental perception of intelligent vehicles. This paper proposes a vehicle detection method based on pseudo-visual search and the histogram of oriented gradients (HOG)–local binary pattern (LBP) feature fusion. Using radar detection information, this method can directly extract the region of interest (ROI) of vehicles from infrared images by imitating human vision. Unlike traditional methods, the pseudo-visual search mechanism is independent of complex image processing and environmental interferences, thereby significantly improving the speed and accuracy of ROI extraction. More notably, the ROI extraction process based on pseudo-visual search can reduce image processing by 40%–80%, with an ROI extraction time of only 4 ms, which is far lower than the traditional algorithms. In addition, we used the HOG–LBP fusion feature to train the vehicle classifier, which improves the extraction ability of local and global features of vehicles. The HOG–LBP fusion feature can improve vehicle detection accuracy by 6%–9%, compared to a single feature. Experimental results show that the accuracy of vehicle detection is 92.7%, and the detection speed is 31 fps, which validates the feasibility of the proposed method and effectively improve the vehicle detection performance in severe weather

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The accuracy and robustness of vehicle cognition in severe weather have always been the focus and difficulty of intelligent vehicle environment perception. This paper proposes a vehicle cognition method based on radar and infrared thermal camera information fusion in severe weather. The fusion of radar and infrared cameras can greatly enrich the completeness of vehicle cognitive information. First, an attention mechanism based on radar guidance information was proposed to extract the vehicle region of interest (ROI). Unlike the traditional ROI extraction method, this method is not disturbed by the environment and does not need complicated calculations, which can quickly and accurately extract ROI for vehicles. Second, based on ROI information, the infrared thermal image is reconstructed and enhanced, which is of great significance to improve the accuracy of vehicle detection for deep learning. Finally, we propose a vehicle depth estimation method based on pixel regression and use multi-scale cognitive information to fuse radar and image targets. The fusion method considering depth information can reduce target confusion and improve fusion robustness and accuracy, especially when vehicles are adjacent to each other. The experimental results show that the vehicle detection accuracy of this method is 95.2% and the vehicle detection speed is 37 fps, which effectively improves the performance of vehicle detection in severe weather.

Biodiesel and dimethyl ether (DME) are two promising alternative fuels. The use of their blends in direct-injection (DI) engines seems beneficial since the blending could not only compromise the viscosity between biodiesel and DME but also could improve atomization by initiating flash boiling. Therefore in this work the combustion and emission characteristics of a DI engine fuelled with biodiesel/DME blends were investigated with DME  per centage varying from 0 to 100 (wt.%). The experiment was carried on a modified diesel engine. The results show that the increased DME  per centage results in retarded ignition time and reduced peak value of heat release rate of premixed combustion, but has little effect on the overall combustion duration. With increasing DME blend ratio, the maximum pressure rise rate is depressed linearly, but the exhaust gas temperature rises for all loads. Also a remarkable decrease in power occurs because of the low heat value of DME. Oxides of nitrogen (NO x) and smoke emissions decreased simultaneously with increasing DME  per centage in the blend fuel. Similar behaviour can be seen with different engine speed. Owing to the improved atomization, high DME blend fuel generates more fine particles. In addition, the increase of DME ratio in the blend fuel also leads to decreased carbon monoxide (CO) emission at high loads but increased HC emissions at low loads.