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-toproduction 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.

Towards Autonomous Planetary Exploration

“…The experiences gathered with the successful and ongoing Mars rover missions like the Mars Exploration Rover Mission (MER) [46] and the Mars Science Laboratory (MSL) [28] as well as earlier considerations on rover autonomy [69] clarify requirements and space-suitable options regarding hardware as well as software components. Autonomous navigation solutions for unstructured and unknown environments taking robot safety, resource management (e. g., power consumption) and general robustness into account are available in many robotic research areas such as autonomous driving, e. g., [67,76], search and rescue [57] and planetary rovers / field robotic systems tested on Earth [26,43,60,65,73]. These systems, including all systems participating in the SpaceBotCamp 2015 challenge, use a variety of optical sensors for navigation, most commonly laser scanners and active RGB-D cameras [14,66] or a combination of those, e. g., [32,60,65].…”

An Automated Electric Vehicle Prototype Showing New Trends in Automotive Architectures

“…To address the requirements for advanced software functions such as intelligent energy management or a function that autonomously parks 1 http://www.projekt-race.de/en, a cooperation of Siemens AG, fortiss GmbH, TRW Automotive GmbH, AVL GmbH, Fraunhofer AISEC, Institute for Aerospace Systems (ILS), RWTH Aachen and Technische Universität München the car onto an inductive charging station, not only software components have been developed, but also a compatible sensor set capable of detecting a charging station and obstacles has been selected and integrated. We also considered the trend seen in autonomous vehicle design (for example [4]) that none of the sensors should be mounted outside of the chassis parts but integrated into the vehicle.…”

Sampling Based Motion Planning for Heavy Duty Autonomous Vehicles

“…Other impressive efforts include the Bertha project by Mercedes in collaboration with Forschungszentrum Informatik and Karlsruhe Institute of Technology (Ziegler et al, 2014b), the Public Road Urban Driverless Car Test (PROUD) by University of Parma (Broggi et al, 2015) and the Stadtpilot project (Nothdurft et al, 2011). Building on the success and inspiration from many of these projects almost all major car manufacturers have now launched projects involving autonomous vehicles and there is a race to be the first company with an autonomous car on the market.…”

“…However, when focusing on fully autonomous driving further improvements of these systems are needed. The PROUD project had extensive use of cameras (Broggi et al, 2015) and the Bertha project uses cameras both for localization and obstacle detection (Ziegler et al, 2014b). Two main approaches for camera based localization is the lane marking and feature based techniques.…”

“…The future trajectories or intentions must be predicted so they can be handled in a safe way while still allowing the autonomous vehicle to progress and not being over conservative. The future trajectories of other vehicles are often predicted using the assumption of constant velocity (Ziegler et al, 2014b;Werling et al, 2008;Ferguson et al, 2008). This assumption is often valid in simpler situations but with increasing complexity of traffic situations, intentions such as lane changes, yielding at intersection or turning needs to be predicted (Liebner et al, 2013;Schlechtriemen et al, 2015;Ward and Folkesson, 2015).…”