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Typically, when one thinks of advanced driver assistance systems (ADAS), systems such as Forward Collision Warning (FCW) and Collision Imminent Braking (CIB) come to mind. In these systems driver assistance is provided based on knowledge about the subject vehicle and surrounding objects. A new class of these systems is being implemented. These systems not only use information on the surrounding objects but also use information on the driver's response to an event, to determine if intervention is necessary. As a result of this trend, an advanced level of understanding of driver braking behavior is necessary. This paper presents an alternate method of analyzing driver braking behavior. This method uses a frequency content based approach to study driver braking and allows for the extraction of significantly more data from driver profiles than traditionally would have been done.

Modern passenger cars are being equipped with advanced driver assistance systems such as lane departure warning, collision avoidance systems, adaptive cruise control, etc. Testing for operation and effectiveness of these warning systems involves interaction between vehicles. While dealing with multiple moving vehicles, obtaining discriminatory results is difficult due to the difficulty in minimizing variations in vehicle separation and other parameters. This paper describes test strategies involving an automated test driver interacting with another moving vehicle. The autonomous vehicle controls its state (including position and speed) with respect to the target vehicle. Choreographed maneuvers such as chasing and overtaking can be performed with high accuracy and repeatability that even professional drivers have difficulty achieving. The system is also demonstrated to be usable in crash testing.