Current Issue : January-March Volume : 2024 Issue Number : 1 Articles : 5 Articles
The number of casualties resulting from collisions between pedestrians and motor vehicles continues to rise. A significant factor is the misunderstanding of vehicle behavior intentions by pedestrians. This is especially true with the continuous development of vehicle automation technology, which has reduced direct interaction between drivers and the outside world. Therefore, accurate communication of vehicle behavior intentions is becoming increasingly important. The purpose of this study is to investigate the impact of external vehicle acceleration signal light on the interaction experience between pedestrians and vehicles. The differences between the use and nonuse of acceleration signal light are compared through controlled test track experiments in real scenarios and in videos.The results show that acceleration signal light help pedestrians understand vehicle behavior intentions more quickly and make safer crossing decisions as well as improving their perception of safety when crossing the street and their trust in vehicle behavior....
Predictive energy-saving control (PEC) is aimed at reducing energy consumption by designing the vehicle speed while considering future road and traffic information. In particular, the slope of the road ahead is necessary and critical for PEC. This paper proposes a road slope prediction method for production vehicles that uses the nonlinear autoregressive (NAR) neural network model based on road slope sensors. To adaptively balance the energy savings and trip time, this paper proposed a real-time variable weight PEC method for a four-wheel-drive (4WD) intelligent electric vehicle. The weight coefficients are automatically changed according to the characteristics of the road slope, where the vehicle energy-saving rate on the steep downhill road can be maximized. The results of real-time simulation on the dSPACE platform indicated that the road slope predictive model can be run in real time and adapted to changes in road slope and speed. The root mean square error (RMSE) of the predictive results is 0.3063. On a steep downhill road, the energy-saving rate of the proposed PEC method can reach 30.87% at a small expense of time of 3.75%. On uphill and flat roads, energy can be saved by 6.35% at a time cost of 3.0%. Compared with the PEC with constant weight factors, the two control objectives of energy savings and traveling time can be better balanced on various types of roads....
A collaborative control strategy for distributed drive electric vehicles (DDEVs) focusing on differential drive assisted steering (DDAS) and active front steering (AFS) is proposed to address the issues of sudden torque changes, reduced steering characteristics, and weak collaborative control capabilities caused by the coupling of the AFS and DDAS systems in DDEVs. This paper establishes a coupled dynamic model of the AFS and DDAS systems and, on this basis, designs AFS controllers for yaw velocity feedback control and DDAS controllers for steering wheel torque control, respectively. Additionally, it analyzes the interference factors of the two control systems and develops a collaborative control strategy for DDAS and AFS; this control strategy establishes a corner motor correction module, steering wheel torque correction module, and assistance correction module. Co-simulation is carried out on Matlab/Simulink and the Carsim platform to verify the correctness of the model under typical working conditions; to reduce the sudden change in the steering wheel torque caused by AFS additional angle interventions; to improve the poor steering characteristics caused by DDAS, introducing additional yaw torque; to greatly enhance the collaborative control effect; and to meet the requirements for vehicle handling stability, portability, and safety....
Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive....
Today, goods transportation is considered to be one of the most important activities of national economics. Logistics and supply chain play an important role in the industry and services, considering the needs of the people, while there is an increase in the population. In addition, the role of logistics in urban areas, especially in restaurants, grocery stores, etc., is clearly visible. Besides, the final price of the goods is the most important factor that is always considered in service and in production. Due to this important factor, transportation has been found to be one of the most significant and influential factors in determining the price of goods. For these reasons, the newest variant of the vehicle routing problem, called the line feeder vehicle routing problem (LFVRP), is considered in this paper, in which various types of vehicles (large and small vehicles) are used for providing services to customers. In this particular type of delivery issue, these vehicles must start from the warehouse, meet customers, and finally return to the depot. In fact, the issue of LFVRP is related to the fast customer service in urban areas because in this case, all that are considered to be of priority are to minimize transportation costs and overall distribution time for fast customer service, especially in urban areas. Due to the many applications of this problem in the real world, a general review of this problem is conducted, and the versions of this problem are described along with the algorithms for its solution in the paper....
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