Jan K. Schiffmann

Comparison of Lidar-Based and Radar-Based Adaptive Cruise Control Systems

Since the late 1980s, Delphi Automotive Systems has been very involved with the practical development of a variety of Collision Avoidance products for the nearand long-term automotive market. Many of these complex collision avoidance products will require the integration of various vehicular components/systems in order to provide a cohesive functioning product that is seamlessly integrated into the vehicle infrastructure. One such example of this system integration process was the development of an Adaptive Cruise Control system on an Opel Vectra. The design approach heavily incorporated system engineering processes/procedures. The critical issues and other technical challenges in developing these systems will be explored. Details on the hardware and algorithms developed for this vehicle, as well as the greater systems integration issues that arose during its development will also be presented. Actual on-road test results of the Adaptive Cruise Control system are discussed and compared for the two types of sensors.

Electronics and Algorithms for Rollover Sensing

Rollover sensing and discrimination generally requires an algorithm that monitors vehicle motion and anticipates conditions that will lead to a rollover. In general, a deploy command is required in a time frame such that safety measures can be activated early enough to protect the occupants. A rollover discrimination system will typically include internal motion sensors, vehicle signals from other on-board sensors, and a microprocessor to execute the deployment algorithm. A supplemental signal path is used to arm the system, making it less susceptible to single point component failures. In this chapter we explore basic concepts of rollover sensors and system mechanization, rollover discrimination algorithms, and arming methodology. A simulation environment that models the performance of the system across part tolerance, temperature extremes and component age is used to estimate the scope of expected discrimination performance in the field. A representative selection of real-world events is presented, which can be used to calibrate algorithm parameters to ensure immunity margins and deployment timing for the entire system.

Model-based scene tracking using radar sensors for intelligent automotive vehicle systems

Several intelligent vehicle applications require knowledge of the presence of objects in the path of the host vehicle. Current approaches to this in-path decision process do not fully utilize the information available from the forward-looking sensor. This paper describes a path algorithm in which the observed trajectories of preceding vehicles are used to determine the shape of the road in front of the host, along with the orientation of the host w.r.t. the road, thereby improving the accuracy of object lane determination.

Identification of in-lane vehicles using a scene tracking technique for intelligent automotive systems

Several Intelligent Vehicle applications require knowledge of the presence of objects in the path of the host vehicle. Current approaches to this in-path decision process do not fully utilize the information available from the forward- looking sensor.An earlier paper described a scene tracking path algorithm in which the observed trajectories of preceding vehicles are used to determine the shape of the road in front of the host, along with the orientation of the host with respect to the road, thereby improving the accuracy of object lane determination. This paper reviews that algorithm and presents simulation-based comparisons of the performances of the conventional and scene tracking approaches.