Autonomous Driving Goes Downtown

By Uwe Franke, Dariu M. Gavrila, S. Görzig, F. Lindner, F. Paetzold, Christian Wöhler. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning to Drive in a Day (2018, background): “a) Mapping approaches: Since early examples [9], [10], autonomous vehicle systems have been designed to navigate safely through complex environments using advanced sensing and control algorithms [11], [12], [13].”
On Learning Vector − Valued Functions (2003, background): “For example, an interesting problem arises in reinforcement learning or control when we need to learn a map between a set of sensors placed in a robot or autonomous vehicle and a set of actions taken by the robot in response to the surrounding environment, see e.g. [18].”
Pedestrian detection and tracking with night vision (2005, background): “jection curve, histogram, and inertia [13]; and principal-component analysis and time-delay neural networks [14].”
Real time detection of lane markers in urban streets (2008, methodology): “Techniques used varied from using monocular [11] to stereo vision [5], [6], using lowlevel morphological operations [2], [3] to using probabilistic grouping and B-snakes [14], [7], [9].”
A Trainable System for Object Detection (2000, unknown): “The DaimlerChrysler Urban Traffic Assistant (UTA) is a real-time vision system for obstacle detection, recognition, and tracking (Franke et al., 1998).”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Motivated by the success of the PROMETHEUS projects to drive autonomously on highways, Franke et al. (1998) describe a real-time vision system for autonomous driving in complex urban traffic situations.”
Artificial vision in road vehicles (2002, unknown): “The research groups of the Universität der Bundeswehr [20] , Daimler-Benz [21] and Robert Bosch GmbH [22] base their road detection functionality on a specific road model: lane markings are modeled as clothoids.”
Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety (2007, background): “Recognition of computer vision as a critical technology for intelligent vehicles can be traced to the earlier efforts dealing with autonomous mobile robots and autonomous driving [3]–[5].”
Survey of Pedestrian Detection for Advanced Driver Assistance Systems (2010, methodology): “In [53], Franke and Gavrila suggest the analysis of gait pattern of pedestrians crossing perpendicular to the camera.”