Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

The incentive policies of new energy vehicles substantially promoted the development of the electrical vehicles technology and industry in China. However, the environmental impact of the key technology parameters progress on the battery electrical vehicles (BEV) is uncertain, and the BEV matching different lithium-ion power batteries shows different environmental burdens. This study conducts a life cycle assessment (LCA) of a BEV matching four different power batteries of lithium-ion phosphate (LFP), lithium-ion nickel-cobalt-manganese (NCM), lithium manganese oxide (LMO), and lithium titanate oxide (LTO) batteries. In addition, the 2025 and 2030 prediction analyses of the batteries production and life cycle BEV are conducted with the specially considered change and progress of the power battery energy density, battery manufacturing energy consumption, electricity structure, battery charge efficiency, and vehicle lightweight level. In addition, sensitivity analyses of power battery energy density, battery manufacturing energy consumption, electricity structure, and battery charge efficiency are conducted. The results show that the LFP battery is more environmentally friendly in the global warming potential (GWP) and acidification potential (AP), and the NCM battery is more environmentally friendly in abiotic depletion (fossil) (ADP(f)) and human toxicity potential (HTP). However, the LTO battery shows the highest environmental impact among the four environmental impact categories due to the lower energy density. For life cycle BEV, GWP and ADP(f) of BEV based on LFP, NCM, and LMO are lower than those of internal combustion engine vehicles (ICEV), while AP and HTP of BEV based on the four batteries are higher than those of ICEV. The grave-to-cradle (GTC) phase of vehicle has substantial environmental benefit to reduce the human toxicity emission. With the improvement of the battery density, battery charge efficiency, electricity structure, and glider lightweight level, life cycle BEVs based on the four different batteries show substantial environmental benefits for four environmental impact categories.

To better understand a drivers driving speed selection behaviour in low illumination, a self-designed questionnaire was applied to investigate driving ability in low illumination, and the influencing factors of low-illumination driving speed selection behaviour were discussed from the drivers perspective. The reliability and validity of 243 questionnaires were tested, and multiple linear regression was used to analyse the comprehensive influence of demographic variables, driving speed in a low-illumination environment with street lights and driving ability on speed selection behaviour in low illumination without street lights. Pearsons correlation test showed that there was no correlation among age, education, accidents in the past 3 years, and speed selection behaviour in low illumination, but gender, driving experience, number of night-driving days per week, and average annual mileage were significantly correlated with speed selection behaviour. In a low-illumination environment, driving ability has a significant influence on a drivers speed selection behaviour. Technical driving ability under low-illumination conditions of street lights has the greatest influence on speed selection behaviour on a road with a speed limit of 120km/h (β=0.51). Risk perception ability has a significant negative impact on speed selection behaviour on roads with speed limits of 80km/h and 120km/h (β=0.25 and β=0.34, respectively). Driving speed in night-driving environment with street lights also has a positive influence on speed selection behaviour in low illumination (β=0.61; β=0.28; β=0.37).

Electric bikes play an important role in the urban transportation system in China. Yellow-light running behavior of riders is one of the most critical factors for e-bike riders involved in traffic crashes at intersection. The main purpose of this study is to explore how a variety of factors affect e-bike riders’ yellow-light running behaviors at intersection by a field observation conducted in Xi’an, China. Based on 396 e-bike riders who faced yellow-light samples, two analytical methods, the principle component analysis logistics model and a base logistics model, were employed to evaluate the impacts of contributing factors on e-bike riders’ yellow-light running behavior. The modeling results showed that seven variables significantly affect the e-bike riders’ yellow-light running behavior, which were the approaching speed of e-bike, the distance to stop line, riders’ age and gender attributes, type of e-bike, and the characteristics of intersection including the width of intersection and the existence of physical barriers. This study can provide valuable insights into understanding e-bike riders’ yellow-light running behavior and may also help decision makers propose countermeasures to reduce e-bike rider-related crashes at intersection.

With the rapid development of science and technology, the automobile industry is also gradually expanding due to which energy security and ecological security are seriously threatened. This paper was aimed at studying the energy organization strategy of power-split hybrid electric vehicles based on a reinforcement learning algorithm. A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by using a planetary gear set to split and combine the power generated by electric machines and a combustion engine. This improves the fuel economy to some extent. However, to increase the fuel economy to a greater extent. This study primarily introduces the hybrid electric vehicle’s structure and presents a reinforcement learning-based management of energy approach for hybrid electric vehicles. It constructs the vehicle power model of HEV and Markov probability transfer model, then designs the energy control strategy based on reinforcement learning, and finally compares it with the energy control strategy based on PID (Proportional-Integral-Derivative). Using MATLAB/Simulink, the cycle conditions of NEDC (New European Driving Cycle) and FTP-75 (Federal Test Procedure) are selected to carry out simulation experiments. The energy management technique suggested in this study, which is based on reinforcement learning, may efficiently enhance the usage rate of automotive gasoline. The replication results show that the fuel consumption per 100 km (kilometers) based on reinforcement learning management strategy is 4.6% and 2.7% lower than the PID management strategy under two working conditions. The economy of fuel of the hybrid electric vehicle is also effectively improved.

Drivers take up the leading role in road traffic system, in all road traffic accidents, the ones initiated by driver factor account for over 90%.The main reason is that such drivers have not formed the “internal pattern” of correctly driving a vehicle in their brain, so that drivers cannot strongly experience the potential danger. When danger comes, drivers still have unawareness, or although they take measures, it is too late to avoid the happening of an accident. This paper has carried out further investigation on the drivers’ danger sense based on a profound analysis of driving behavior. 3DS MAX is used to implement dangerous scene cartoons, which can form one continuous driving time and space, so that drivers trained can observe at some points, which can provide understanding of sense organ for drivers. Furthermore, the method of hall type discussion is adopted to improve drivers’ psychological ability to bear and potential risk cognitive level. Such we have made is to make the psychology experiences for drivers under the precarious position in person experienced, to offer experience for storing the correct “interior mode”, finally reduce the traffic conflict and the probability of traffic accidents