For decades, radar has been applied extensively in warfare, earth observation, rain detection, and industrial applications. All those areas are characterized by requirements such as high quality of service, reliability, robustness in harsh environment and short update time for environmental perception, and even imaging tasks. In the vehicle safety and driver assistance field, radars have found widespread application globally in nearly all vehicle brands. With the market introduction of the 2014 Mercedes-Benz S-Class vehicle equipped with six radar sensors covering the vehicles environment 360 • in the near (up to 40 m) and far range (up to 200 m), autonomous driving has become a reality even in low-speed highway scenarios. A large azimuth field of view, multimodality and a high update rate have been the key innovations on the radar side. One major step toward autonomous driving was made in August 2013. A Mercedes-Benz research S-Class vehicle-referred to at Mercedes as Bertha-drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany. It followed the well-known historic Bertha Benz Memorial Route. This was done on the basis of one stereo vision system, comprising several long and short range radar sensors. These radars have been modified in Doppler resolution and dramatically improved in their perception capabilities. The new algorithms consider that urban scenarios are characterized by significantly shorter reaction and observation times, shorter mean free distances, a 360 • interaction zone, and a large variety of object types to be considered. This paper describes the main challenges that Daimler radar researchers faced and their solutions to make Bertha see.INDEX TERMS Radar, automotive radar, autonomous driving.

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Recent literature has introduced several methods for processing an out-of-sequence measurement (OOSM) in tracking for both the 1-step-lag and l-step-lag case. However, in a realistic tracking application, a data association algorithm, such Joint Probabilistic Data Association (JPDA) must be used in order to associate measurements with tracks in a cluttered environment. This paper investigates the OOSM problem in tracking for both the 1-step-lag and l-step-lag cases using JPDA. The OOSM algorithms presented in the literature are extended to support JPDA and the results are analyzed. The resulting algorithms are then applied to an automotive frontal pre-crash system, where OOSM and JPDA are vital factors in improving the performance of such a system.