Making Bertha Drive—An Autonomous Journey on a Historic Route

By Julius Ziegler, Philipp Bender, M. Schreiber, Henning Lategahn, T. Strauß, Christoph Stiller, T. Dang, Uwe Franke, N. Appenrodt, Christoph Gustav Keller and 21 more. 2014

842 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Environmental Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Bertha [53] is developed by Daimler and has 4 120 ◦ short-range radars, two long-range range radar on the sides, stereo camera, wide angle-monocular color camera on the dashboard, another wide-angle camera for the back.”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, neutral): “CONTENTS I Introduction 2”
Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions (2015, neutral): “Typically, this applies to public road tests or field operational tests like the Bertha-Benz drive [78].”
Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs (2016, methodology): “Recall that the AUV must be capable of sensing its surroundings and of self-driving without human inputs.(8) To this end, it uses a myriad of on-board sensors, ranging from radio detection and ranging (RADAR), GPS, video cameras to controller area network (CAN) bus sensors that monitor vehicle’s int”
A Review of Motion Planning Techniques for Automated Vehicles (2016, methodology): “work has been already started by KIT [5] and CMU [108].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “In 2014, Ziegler et al. (2014) demonstrated a 103 km ride from Mannheim to Pforzheim Germany, known as Bertha Benz memorial route, in nearly fully autonomous manner.”
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather (2019, background): “The most promising autonomous vehicle systems rely on redundant inputs from multiple sensor modalities [59, 6, 74], including camera, lidar, radar, and emerging sensor such as FIR [30].”
Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments (2017, background): “V EHICLE localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS) [1].”
Conditional Affordance Learning for Driving in Urban Environments (2018, background): “ and our trained models can be found at https://github.com/xl-sr/CAL. 2 2 Related Work Modular pipelines are the most popular approach to autonomous driving and are employed by most research projects [4,5]. To build the environmental model, the perception stack needs to detect all aspects of the tra”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “An interesting observation is that even the algorithms that utilize more precise lane maps (built manually) do not reach the critical accuracy (around 20 cm [109]) for automated driving.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “LiDAR is widely used in object detection, distance estimation, edge detection, Simultaneously Localization and Mapping (SLAM) [38], [11], and High-Definition (HD) Map generation [39], [40], [41], [12].”
A functional architecture for autonomous driving (2015, background): “However, each allocation has slightly diﬀerent responsibilities and scope of operation.”
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving (2020, background): “The first one is the more conventional, model-based, modular pipeline approach [3]–[10].”
Deep Object-Centric Policies for Autonomous Driving (2018, neutral): “These modular pipelines with rule-based system dominate autonomous driving industry [6], [7], [8].”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, methodology): “2) Graph Neural Network Using Vector Maps: Benz [136] ﬁrst applies HD maps to trajectory prediction, and executes map topology based on the lane information associated with the vehicle to obtain its future trajectory along the lane.”