Juergen Dickmann

Dynamic level of detail 3D occupancy grids for automotive use

In this paper, a generic approach for three-dimensional environment representation is presented. Scans from range finders are accumulated into a three-dimensional occupancy grid. A probabilistic measurement model is used to represent measurement uncertainties. Free regions are modelled as well and contribute to a precise representation of the environment. In order to acquire a feasible three-dimensional grid, a hierarchical data structure is proposed. As a positive result, the level of detail and respectively the grid resolution is controllable by the application or by the content. As an example application, it will be shown how height information can be derived from a sensor with one horizontal scan plane only.

Automotive active safety & comfort functions using radar

Increase of safety and comfort is a main objective of car manufacturers as well as of their suppliers and a substantial distinguishing feature in the international competition. Active safety systems as well as driver assistance systems are becoming more and more important. They help to recognize dangerous situations at an early stage, and thus, help to avoid accidents or at least to reduce the accident severity, especially within the permanently increasing traffic density. Radar sensors play a decisive roll for the environment perception. After an introduction to the evolution of automotive systems, commonly implemented functions using radar will be discussed. Finally, as an example, the arrangement of the radar sensors in the new Mercedes-Benz E-Class and their role in the adjacent environment perception system - as a part of the safety and comfort functions - will be outlined.

Results of a precrash application based on Laserscanner and short range radars

In this paper, we present a vehicle safety application based on data gathered by a laserscanner and two short range radars that recognizes unavoidable collisions with stationary objects before they take place in order to trigger restraint systems. Two different software modules are compared that perform the processing of raw data and deliver a description of the vehiclepsilas environment. A comprehensive experimental evaluation based on relevant crash and non-crash scenarios is presented.

Evaluation of different approaches for road course estimation using imaging radar

This work presents three imaging radar sensor approaches to estimate road courses, needed by intelligent vehicle systems such as active cruise control or collision avoidance. Two of the approaches use gridmap data. A gridmap integrates each measurement in a chronological order. The third approach analyzes moving objects ahead of the ego vehicle. One approach has been published previously, the other two are new. A range estimation is necessary on country roads and completes each approach. All approaches are evaluated using a huge dataset of country roads. The driven trajectory is taken as ground truth for the evaluation. The advantages and disadvantages are determined for each approach. The results show the new approach based on gridmap data performs up to 78% better than the known one. The other new approach using moving objects as input information yields estimations which are about three times more accurate than the ones from the known approach.

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.

Parking space detection with hierarchical dynamic occupancy grids

An automatic parking system relies on precise estimation of parking space geometry.

Radar-interference-based bridge identification for collision avoidance systems

Automotive radar sensors are commonly used for providing environment information required by driver assistance systems. Although they show a good performance in measuring the distance and the speed of other objects even under poor weather conditions, they suffer from the shortcoming of a missing resolution in elevation. Consequently bridges crossing the lane of the ego vehicle may look similar to stationary obstacles. This contribution shows a bridge identification algorithm based on the interference pattern resulting from the multipath propagation of the radar wave. During the approaching to bridges or stationary obstacles, the pattern leads to a variation in the backscattered power from the object. This variation can be used to make statements about the object height position above the road. Results calculated from real scanning radar sensor data show the usability in real traffic scenarios.

Analysis of automobile scattering center locations by SAR measurements

Synthetic aperture radar (SAR) is known to have the capability for a range independent high-resolution cross range processing. In this paper the SAR processing is used to localize the scattering centers of an automotive target at 24 GHz and 77 GHz at different bandwidths. Furthermore, the combination of digital beam forming (DBF) with SAR initiates the opportunity to determine the exact location of the scattering center in azimuth, height, and range.

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.

Geometric-Model-Free Tracking of Extended Targets Using 3D-LIDAR-Measurements

Tracking of extended targets in high definition, 360-degree 3D-LIDAR (Light Detection and Ranging) measurements is a challenging task and a current research topic. It is a key component in robotic applications, and is relevant to path planning and collision avoidance. This paper proposes a new method without a geometric model to simultaneously track and accumulate 3D-LIDAR measurements of an object. The method itself is based on a particle filter and uses an object-related local 3D grid for each object. No geometric object hypothesis is needed. Accumulation allows coping with occlusions. The prediction step of the particle filter is governed by a motion model consisting of a deterministic and a probabilistic part. Since this paper is focused on tracking ground vehicles, a bicycle model is used for the deterministic part. The probabilistic part depends on the current state of each particle. A function for calculating the current probability density function for state transition is developed. It is derived in detail and based on a database consisting of vehicle dynamics measurements over several hundreds of kilometers. The adaptive probability density function narrows down the gating area for measurement data association. The second part of the proposed method addresses weighting the particles with a cost function. Different 3D-griddependent cost functions are presented and evaluated. Evaluations with real 3D-LIDAR measurements show the performance of the proposed method. The results are also compared to ground truth data.