Pedestrian crash causation analysis and active safety system calibration.

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers' own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle-pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model.

The severity of vehicle-pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle-pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle-pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle-pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

Squeeze-Printing Ultrathin 2D Gallium Oxide out of Liquid Metal for Forming-Free Neuromorphic Memristors.

Two-dimensional (2D) metal oxides exhibit extraordinary mechanical and electronic properties, leading to new paradigms in the design of electronic and optical systems. However, as a representative, a 2D Ga2O3-based memristor has rarely been touched, which is hindered by challenges associated with large-scale material synthesis. In this work, the ultrathin 2D Ga2O3 layer (∼3 nm thick) formation on the liquid gallium (Ga) surface is transferred with lateral dimensions over several centimeters on a substrate via the squeeze-printing strategy. 2D Ga2O3-based memristors exhibit forming-free and bipolar switching behaviors, which also reveal essential functions of biological synapse, including paired-pulse facilitation, spiking timing-dependent plasticity, and long-term depression and potentiation. These results demonstrate the potential of 2D Ga2O3 material for neuromorphic computing and open up an avenue for future electronics application, such as deep UV photodetectors, multimode nanoresonators, and power switching devices.