Matthias Goebl

Bio: Matthias Goebl is an academic researcher from Technische Universität München. The author has contributed to research in topics: Autonomous system (mathematics) & Active vision. The author has an hindex of 7, co-authored 11 publications receiving 285 citations.

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

Abstract: This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. © 2008 Wiley Periodicals, Inc.

Team AnnieWAY’s Autonomous System for the DARPA Urban Challenge 2007

Abstract: This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner which delivers both range and reflectivity measurements. While range measurements are used to provide 3D scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a concurrent hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition.

Design and capabilities of the Munich Cognitive Automobile

Abstract: This paper presents the design of the cognitive automobile in Munich. The focus of the capabilities shown here is the navigation on highways and rural roads. The emphasis on higher speed requires early detection of far field objects, so a multi focal active vision with gaze control is essential. For increased robustness lidar range sensors are combined with vision using an object fusion approach. An elaborate safety concept and a verification stage ensure a safe behavior of the vehicle in all situations. A communication system enables the vehicle to perform cooperative perception and action together with similar intelligent vehicles.

Kognitive Automobile - Neue Konzepte und Ideen des Sonderforschungsbereiches TR-28

Abstract: Zusammenfassung: Die folgende Arbeit gibt einen Uberblick uber den Aufbau des kognitiven Automobils des Sonderforschungsbereiches TR/28 in Munchen. Ausgehend von den Sensoren zur Wahrnehmung des Fahrzeugumfeldes werden die Methoden und Verfahren zur Strasenund Objektdetektion aufgezeigt. Dabei wird die multifokale Kameraplattform mit Blickrichtungssteuerung durch den Einsatz zweier Lidarsensoren unterstutzt. Die Objekthypothesen der beiden Systeme werden anschliesend durch einen propabilistischen Ansatz fusioniert. Damit kann ein Lagebild erstellt werden, auf dessen Grundlage die Pfadplanung Entscheidungen treffen kann. Um einen sicheren autonomen Betrieb zu gewahrleisten, wurde ein strukturiertes Sicherheitskonzept und eine zusatzliche Verifikationsstufe zu Grunde gelegt. Die Bildung von kooperativen Gruppen mehrerer kognitiver Fahrzeuge wird durch ein Kommunikationsstufe ermoglicht.

Eine realzeitfähige Architektur zur Integration kognitiver Funktionen

Abstract: Diese Arbeit prasentiert eine echtzeitfahige Hard- und Softwarearchitektur fur kognitive Systeme. Eine leistungsfahige Rechnerplattform mit mehreren Prozessorkernen und -knoten verarbeitet alle Daten. Durch ein flexibles Prozessmodell laufen darauf Tasks mit verschiedenen Zeitanforderungen. Die entwickelte Realzeitdatenbasis fur kognitive Automobile (KogMo-RTDB) ermoglicht als zentrale Schnittstelle den reibungslosen Datenaustausch zwischen Echtzeit- und Nicht-Echtzeit-Tasks. Ein neuer Algorithmus fur ein blockierungsfreies Schreib-/Leseprotokoll sorgt fur Datenkonsistenz. Sein Historienkonzept ermoglicht die zeitliche Entkopplung. Die ruckwirkungsfreie Methode zur Datenaufzeichnung unterstutzt die Simulation. Eine Analyse beweist die Echtzeitfahigkeit, zahlreiche Messungen bestatigen die Effizienz des Gesamtsystems. Die Praxistauglichkeit zeigt der erfolgreiche Einsatz in mehreren kognitiven Automobilen und in weiteren Anwendungen.

A Review of Motion Planning Techniques for Automated Vehicles

Abstract: Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

Making Bertha Drive?An Autonomous Journey on a Historic Route

Abstract: 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.

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Abstract: Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.

Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments

Abstract: We describe a practical path-planning algorithm for an autonomous vehicle operating in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. The core of our approach to path planning consists of two phases. The first phase uses a variant of A* search (applied to the 3D kinematic state space of the vehicle) to obtain a kinematically feasible trajectory. The second phase then improves the quality of the solution via numeric non-linear optimization, leading to a local (and frequently global) optimum. Further, we extend our algorithm to use prior topological knowledge of the environment to guide path planning, leading to faster search and final trajectories better suited to the structure of the environment. We present experimental results from the DARPA Urban Challenge, where our robot demonstrated near-flawless performance in complex general path-planning tasks such as navigating parking lots and executing U-turns on blocked roads. We also present results on autonomous navigation of real parking lots. In those latter tasks, which are significantly more complex than the ones in the DARPA Urban Challenge, the time of a full replanning cycle of our planner is in the range of 50—300 ms.

Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme

Abstract: A common prerequisite for many vision-based driver assistance systems is the knowledge of the vehicle's own movement. In this paper we propose a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images. Our method is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed. The only assumption we make is a known camera geometry, where the calibration may also vary over time. We employ an Iterated Sigma Point Kalman Filter in combination with a RANSAC-based outlier rejection scheme which yields robust frame-to-frame motion estimation even in dynamic environments. A high-accuracy inertial navigation system is used to evaluate our results on challenging real-world video sequences. Experiments show that our approach is clearly superior compared to other filtering techniques in terms of both, accuracy and run-time.