The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.

The occupant\'s posture can be changeable to an inadvertent or unintentional out-of-seat position (OOSP) depend on their convenience. Understanding for OOSP has been demanded, but it is not sufficient; especially when AEB is activated. The aim of the current study was to characterize the motion responses of an occupant in various OOSPs when AEB is activated and to identify if there were any additional risks of injury or discomfort to the occupant. The normal seat position (NSP) and three OOSPs were defined to compare the difference of human responses, and six healthy males were participated. Particularly, the maximum rotation angles of the neck in OOSP2 and OOSP3 differed significantly around 1.3 ± 0.3 and 1.4 ± 0.2 times higher respectively than from in the NSP (p < 0.05). Occupants assuming OOSP3 exhibited motion characteristics were not restrained effectively and characterized a hovering and falling upper body and a slipping pelvis. This study has identified, for the first time, a potential risk of injury or discomfort when AEB is activated while an occupant is in an OOSP. This study may serve as fundamental data for the development of safety system that can improve restraint and counteract any deterioration in occupant safety.

Automobiles carrying an autonomous emergency braking system (AEBS) are currently prevailing. While the reduction of traffic accidents is expected because of the widespread use of the system, concerns as regards many drivers using the system without proper understanding of the trigger conditions (TCs) have arisen. This research aims to grasp the degree of recognition of the AEBS TCs by a driver with a vehicle equipped with the system.
Using a web research company, we sent a survey sheet for screening to 9999 monitors randomly selected by gender and age group and confirmed own vehicle with an autonomous braking system ownership status. The number of answer targets was 200 for each of the four groups divided by age and gender. In this research, we developed a multivariate analysis model with the degree of understanding the AEBS TCs as the objective variable. The explanatory variables of this model were \"Driver characteristics\" and \"Contact opportunities of information on the AEBS.\"
Using PCA\'s main component scores as the objective variable, two types of multiple regression models were constructed according to the AEBS TCs (do not work properly and work accidentally). The model analysis showed that gender, age, confidence in driving skill, and experience of the AEBS before purchasing are significant variables in both models. The recognition of the conditions of the \"AEBS does not work properly\" was influenced by the information-gathering ability and the degree of reference to various information. In contrast, the recognition of the conditions of the \"AEBS work accidentally\" was influenced by the interest of automobiles, such as the importance of automobile for self-expression and explanation taken up in a car magazine.
This study clarified the driver characteristics and contact opportunities of information that have problems in recognizing the AEBS TCs. Practical Applications: Considering measures, such as public relations, utilizing this result will be meaningful in terms of road safety in the current stage, which is the transitional period of the AEBS.