The objective of this paper is to find object based solutions for a collision avoidance system. In this paper, the authors present an algorithm for obstacle detection, from the actual video images taken by an on-board camera. The proposed technique is based on Histograms of Oriented Gradient (HOG) to extract features of the objects and classify the obstacles by the Time Delay Neural Network (TDNN). The experimental results showed that it can detect general objects, and is not restricted to vehicles, objects or pedestrians. It has provided good results along with high accuracy and reliability.
In recent years, an angular-velocity-based brain injury criterion, BrIC, has been proposed by the National Highway Traffic Safety Administration (NHTSA) for consumer vehicle safety assessment tests. In this study, the cumulative strain damage measure (CSDM), as one of the brain injury metrics, was calculated based on data obtained for a total of 360 anthropomorphic test devices (ATDs) in vehicle crash tests conducted by NHTSA and the Insurance Institute for Highway Safety (IIHS) using the Simulated Injury Monitor (SIMon ver. 4.0), a human brain finite element model developed by NHTSA’s research institute. Self-Organizing Maps (SOMs) and hierarchical clustering were used to classify test data composed of the brain injury risk level based on CSDM and its corresponding head kinematic parameters. Results demonstrated that, in addition to the peak values of angular velocities, the peak values of angular accelerations around three axes are also influential parameters for accurately predicting brain injury risk based on CSDM.