Kristofer D. Kusano

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This paper examines the potential effectiveness of the following three precollision system (PCS) algorithms: 1) forward collision warning only; 2) forward collision warning and precrash brake assist; and 3) forward collision warning, precrash brake assist, and autonomous precrash brake. Real-world rear-end crashes were extracted from a nationally(More)
This study developed and presented a computational model of road departure collisions which used data collected from real-world crashes as the basis for simulations of collision avoidance for vehicles equipped with Lane Departure Warning (LDW) systems. Real-world collisions were extracted from a database of 890 serious road departure crashes with detailed(More)
To mitigate the severity of rear-end and other collisions, Pre-Crash Systems (PCS) are being developed. These active safety systems utilize radar and/or video cameras to determine when a frontal crash, such as a front-to-back rear-end collisions, is imminent and can brake autonomously, even with no driver input. Of these PCS features, the effects of(More)
This study presents the estimated safety benefits of an autonomous pre-crash braking system for both the striking vehicle and collision partner, or struck vehicle, in rear-end collisions. Occupants of the striking vehicle in rear-end collisions are expected to benefit from autonomous pre-crash braking. Often overlooked, however, are the safety benefits to(More)
Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that have the potential to help prevent/mitigate crashes and injuries in intersection crashes. I-ADAS may use side-looking sensors, e.g. radar and lidar, in order to detect potential collisions with vehicles from crossing paths. The success of I-ADAS depends on the range and(More)
There are approximately 4,500 traffic fatalities at intersections each year in the U.S. One method for reducing these crashes is through equipping vehicles with Intersection Advanced Driver Assistance Systems (I-ADAS) that can detect and alert the driver of an impending crash. However, the effectiveness of these systems is expected to be highly dependent on(More)
Naturalistic Driving Studies (NDS) are becoming an integral tool for development of driver assistance systems. Because of its large volume, one challenge with working with NDS data is identifying driving scenarios of interest automatically. This study introduces a methodology for identifying situations where the driver of the instrumented vehicle applied(More)
Frontal Pre-Collision Systems (PCS) and Lane Departure Warning (LDW) systems are two of the first active safety systems to penetrate the passenger vehicle market. PCS can warn the driver, amplify the driver's braking effort, and autonomously brake even if there is no driver input. LDW systems deliver a warning to the driver when the vehicle is drifting out(More)
This study examines the potential effectiveness of a Pre-Collision System (PCS) that integrates Forward Collision Warning (FCW), Pre-crash Brake Assist (PBA), and autonomous Pre-crash Braking (PB). Real-world rear-end crashes were extracted from NASS/CDS years 1993-2008. The sample of 1,396 collisions, corresponding to 1.1 million crashes, was simulated as(More)
Automated Crash Notification (ACN) algorithms utilize telemetric data from vehicles involved in collisions to notify the appropriate emergency services with the aim to elicit the appropriate medical response. One vital piece of telemetric data is the Principal Direction of Force (PDOF) of the collision, which can be determined from data stored in the Event(More)