The vehicle impact velocity and vehicle front-end shape are 2 dominant factors that influence the pedestrian kinematics and injury severity. A significant reduction of all injuries can be achieved for all vehicle types when the vehicle impact velocity is less than 30 km/h. Vehicle designs consisting of a short front-end and a wide windshield area can protect pedestrians from fatalities. The results also could be valuable in the design of a pedestrian-friendly vehicle front-end shape. [Supplementary materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention for the following free supplemental resource: Head impact conditions and injury parameters in four-type vehicle collisions and validation result of the finite element model of one-box vehicle and minicar. ].

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Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting nearmiss incidents based on database instances, we created a largescale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at nearmiss recognition, 61.3% vs. 78.7% at near-miss detection).