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Road departure is a leading cause of fatal crashes in the US and half of all the crashes are related to road departure [1]. Road departure warning (RDW) and road keeping assistance (RKA) are the new active safety areas to be explored. Most of the currently available road-departure detection technologies rely on the detection of lane markings, which are either missing or unclear in many roads. Therefore, in additional to the these lane markings, next-generation road departure detection should rely on the detection of other road edge and boundary objects. Common road edge and boundary indicators include lane marking, grass, curb, metal guardrail, concrete divider, traffic barrels and cones. This paper investigates the distribution of major types of road edges and road boundaries in the United States in order to enhance and evaluate the capabilities and effectiveness of RDW and RKA.

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

Driver state sensing technologies start to be widely used in vehicular systems developed from different manufacturers. To optimize the cost and minimize the intrusiveness towards driving, majority of these systems rely on in-cabin camera(s) and other optical sensors. With their great capabilities of detecting and intervening driver distraction and inattention, these technologies might become key components in future vehicle safety and control systems. However, currently there are no common standards available to compare the performance of these technologies, thus it is necessary to develop one standardized process for the evaluation purpose.