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Lane detection is an important component in automatic pilot system and advanced driving assistance system (ADAS). The stability and precision of lane detection will directly determine precision of control and lane plan of vehicles. Traditional mechanical vision lane detection approaches in complicated environment have the deficiencies of low precision and feature semantic description disabilities. But the lane detection depending on deep learning, e.g. SCNN network, LaneNet network, ENet-SAD network have imbalance problems of splitting precision and storage usage. This paper proposes an approach of high-efficiency deep learning Segnet-LSTM semantic segmentation network. This network structure is composed with encoding network and corresponding decoding networks. First, convolution and maximum pooling. The proposal extracts texture details of five images and stores searching position of maximum pooling. Meanwhile, it will implement interpolate processing to the lost points.

How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements.

The non-contact overload recognition method refers to the method of detecting the vibration state of the vehicle through visual recognition without touching the vehicle, and then calculating the vehicle load in combination with the vehicle dynamics model to determine whether the passing vehicle is overloaded. Due to the convenience of detection, low cost of infrastructure and informatization, this method has great advantages in the field of overload identification. However, the model used in this recognition method is the single mass vibration model at present, which will have a large error due to the interaction between the front and rear suspension, and the position of the center of mass needs to be acquired in the recognition process, which is difficult in the actual identification process. In this paper, a vehicle vibration model containing two modes of vibration is proposed, and uses Sobol algorithm to analyze the parameter sensitivity of the model.

Regenerative braking is an effective technology to extend the driving range of electrified vehicles by recovering kinetic energy from braking. This paper focuses on the design of the regenerative braking control strategy for a commercial vehicle which requires significantly larger braking power than passenger cars. To maximize the energy recovery while ensuring the braking efficiency of the vehicle and its braking safety, this paper proposed a fuzzy logic strategy for regenerative braking control, and a feasibility study was conducted for an electric van. The work includes in three steps. Firstly, state variables that significantly affect regenerative braking performance, i.e., vehicle speed, battery State-of-Charge (SOC), and braking intensity, are identified based on mathematical modelling of the vehicle system dynamics in braking maneuver.

With the development of economy, the road cleaning faces great challenges because the road area keeps increasing and the road types tend to be diversified. Cleaning vehicle is widely used in road surface cleaning, but it is more and more difficult to meet the demand of road surface cleaning only through using a single road surface cleaning method. If the way of manual adjustment of cleaning parameters is adopted, the driver is required to have rich experience. At present, there is an urgent need for a cleaning vehicle that can autonomously adjust cleaning parameters according to the road surface. This study is based on road recognition technology. After the pavement category is reflected by the visual sensor feedback information and the pavement adhesion coefficient, the parameters of the cleaning vehicle are adjusted by the controller to adapt to different roads.