Hans-Ludwig Bloecher

Making Bertha See Even More: Radar Contribution

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.

Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding

An overview on state of the art automotive radar usage is presented and the changing requirements from detection and ranging towards radar based environmental understanding for highly automated and autonomous driving deduced. The traditional segmentation in driving, manoeuvering and parking tasks vanishes at the driver less stage. Situation assessment and trajectory/manoeuver planning need to operate in a more thorough way. Hence, fast situational up-date, motion prediction of all kind of dynamic objects, object dimension, ego-motion estimation, (self)-localisation and more semantic/classification information, which allows to put static and dynamic world into correlation/context with each other is mandatory. All these are new areas for radar signal processing and needs revolutionary new solutions. The article outlines the benefits that make radar essential for autonomous driving and presents recent approaches in radar based environmental perception.

Extraction of Micro-Doppler Signatures using Automotive Radar Sensors

Abstract Detection of pedestrians in an urban environment is a highly sophisticated task. This paper presents a signal processing technique suitable for frequency-modulated continuous wave (FMCW) radar sensors using chirp sequence modulation, allowing observation of slow-moving objects with high resolution capability in range and velocity. Velocity resolution can be improved through auto-regressive linear prediction (AR-LP) without requiring a long chirp sequence duration. Furthermore, the influence of the frequency deviation used and velocity resolution will be shown in a scenario with two objects. An adaptive velocity resolution improvement will be presented to extract a characteristic micro-Doppler signature (µD-signature) of a slowly moving pedestrian, a car, and an inline-skater. This is verified by measurements at the carrier frequency of 77 GHz.