Nils Appenrodt

Making Bertha Drive?An Autonomous Journey on a Historic Route

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments

In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using global nearest neighborhood (GNN) method. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.

Results of a Precrash Application Based on Laser Scanner and Short-Range Radars

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

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.

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.

Occupancy grid map-based extended object tracking

Robust tracking of extended objects plays a major role in research on highly automated driving applications and advanced driver assistance systems. This paper proposes a new approach to estimate the extension of dynamic objects based on object-local occupancy grid maps. This enables estimating free-formed object shapes while being robust against errors coming from, e.g., incorrect segmentation or association. Its benefit is shown based on 4-layer laser scanner sensor data and is evaluated against a ground truth based on a D-GPS system fused with a highly accurate IMU.

Present research activities and future requirements on automotive radar from a car manufacturer??s point of view

Automotive Radar has already found its way into nearly all car manufacturers portfolio, even for small car platforms. Over the decades, the performance requirements increased steadily from simple detector tasks in blind spot monitoring systems to multi range smart environment perception sensors. The utmost push in performance requirement is initiated with the trend towards highly automated driving. Future automotive radar systems have to provide imaging like capabilities and have to interact in radar networks, which allow for highly comprehensive perception tasks. The paper will provide an overview on state of the art automotive radar usage on the basis of the DAIMLER car platforms, will give an outline on future requirements for highly automated driving and will present recent approaches in radar based environmental perception.

Autonomes Fahren auf der historischen Bertha-Benz-Route

Zusammenfassung Im Jahre 1888 trat Bertha Benz die erste Überlandfahrt in der Geschichte des Automobils an. 125 Jahre später wiederholte die Mercedes Benz S-Klasse S 500 Intelligent Drive diese historische Fahrt von Mannheim nach Pforzheim – selbständig, ohne Fahrereingriff und im realen Verkehr. Die Bertha-Benz-Route ist 103 km lang und zeichnet sich durch eine breite Vielfalt von zu bewältigenden Fahrsituationen aus, die repräsentativ für den heutigen Alltagsverkehr sind. Die Strecke beinhaltet die Innenstädte von Mannheim und Heidelberg sowie die Durchfahrung von 23 Ortschaften und kleineren Städten. Zu den Situationen, die ein autonomes Fahrzeug auf der Bertha-Benz-Route beherrschen muss, gehören z. B. Kreisverkehre, Kreuzungen mit und ohne Ampelanlagen, Zebrastreifen, Überholen von Radfahrern oder enge Ortsdurchfahrten mit entgegenkommendem Verkehr. Eine Besonderheit des vorgestellten Projektes war die ausschließliche Verwendung seriennaher Sensorik. Kameras und Radarsensoren in Verbindung mit einer präzisen digitalen Karte ermöglichten die Erfassung des Fahrzeugumfelds auch in komplexen Situationen. Dieser Artikel liefert eine Systemübersicht des Fahrzeugs. Er beschreibt die kamerabasierte Umgebungswahrnehmung, die verwendeten digitalen Karten und die kartenrelative Selbstlokalisierung sowie die Manöverplanung in komplexen Verkehrsszenarien.

Probabilistic rectangular-shape estimation for extended object tracking

This paper presents new methods for the representation of a vehicles contour by an oriented rectangle, also known as the bounding box. The parameters of this bounding box are originally modeled probabilistically by a single multivariate Gaussian distribution. This approach incorporates the sensor uncertainties, where the problem of estimating the parameters of this distribution from range measurements is addressed. Additionally, a transformation of the parameters into the measurement space is introduced. This representation is used to perform probabilistic updates by new measurements. The proposed method can handle strong parameter changes which might be affected by object occlusion. Experiments on real-world data demonstrate the robustness and accuracy of the probabilistic approach integrated in a tracking framework incorporating the Doppler measurements of automotive radars and laser measurements.