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The two-wheeler is a vehicle that runs on two wheels, which is classified as motorcycle, electric-bicycle, and bicycle in this research. China has the largest number of two-wheelers and relevant accidents in the world. The two-wheeler riders have a high level of vulnerability, creating a significant necessity to better understand the characteristics according to the road-user group. The objective of this paper is to study the characteristics and analyze the causes of two-wheeler accidents in China using the CIDAS (China In-Depth Accident Study) Database. 2012 cases of two-wheeler accidents with riders injured or dead were collected from the CIDAS Database from 5 cities (Changchun, Beijing, Weihai, Ningbo and Foshan) in China over a period of 5 years (2011.07-2016.06).

In China, nearly 25% of traffic fatalities are pedestrians. To avoid those fatalities in the future, rapid development of countermeasures within both passive and active safety is under way, one of which is autonomous braking to avoid pedestrian crashes. The objective of this work was to describe typical accident scenarios for pedestrian accidents in China. In-depth accident analysis was conducted to support development of test procedures for assessing Autonomous Emergency Braking (AEB) systems. Beyond that, this study also aims for estimating the mitigation of potential crash severity by AEB systems. The China In-depth Accident Study (CIDAS) database was searched from 2011 to 2014 for pedestrian accidents. A total of 358 pedestrian accidents were collected from the on-site in-depth investigation in the first phase of CIDAS project (2011-2014).

The aim of the study was to investigate the difference between car-to-e-bikes and car-to-pedestrian accidents. The China In-depth Accident Study (CIDAS) database was searched from 2011 to 2013 for pedestrians and e-bikes struck by car, van and SUV fronts, which resulted in 104 pedestrian and 85 e-bike cases where information was sufficient for in-depth analysis. Reconstruction by PC-Crash was performed for all of the sampled cases. Pre-crash parameters were calculated by a MATLAB code. Focus was on prototypical accident scenarios and causes; speed as well as possible prevention countermeasures. It has been shown that traffic light violations, road priority violations, and unsure safety (these situations included misjudgments, unpreparedness, proximity to other road users, inappropriate speeds, etc.…) are the main causes in both the VRU groups. Distinctions were found for aspects of car collision speed, accident scenario, distribution of head contact points and so on.