G. Farber

Bio: G. Farber is an academic researcher. The author has contributed to research in topics: Raw data & Software architecture. The author has an hindex of 1, co-authored 1 publications receiving 80 citations.

A Real-Time-capable Hard-and Software Architecture for Joint Image and Knowledge Processing in Cognitive Automobiles

Abstract: Cognitive automobiles consist of a set of algorithms that cover a wide range of processing levels: from low-level image acquisition and feature extraction up to situation assessment and decision making. The modules implementing them are naturally characterized by decreasing data rates at higher levels, because raw data is discarded after evaluation, and increasing processing intervals, as knowledge based levels require longer computation times. The architecture presented in this papers offers a method to interchange information with different temporal resolutions liberally among modules with distinct cycle times and realtime demands. It allows effortless buffering of raw data for subsequent data fusion and verification, facilitating innovative processing structures. The paper is completed by measurements demonstrating the achieved real-time capabilities on our selected hardware architecture.

Are we ready for autonomous driving? The KITTI vision benchmark suite

Abstract: Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti

Vision meets robotics: The KITTI dataset

Abstract: We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

Reachability Analysis and its Application to the Safety Assessment of Autonomous Cars

Abstract: This thesis is about the safety verification of dynamical systems using reachability analysis. Novel solutions have been developed for classical reachability analysis, stochastic reachability analysis, and their application to the safety assessment of autonomous cars. Classical reachability analysis computes the set of states that can be reached by a system. If the reachable set does not intersect any set of unsafe states, the safety of the system is guaranteed. Algorithms for this problem have been developed for linear, nonlinear, and hybrid systems. Stochastic reachability analysis measures the probability of reaching an unsafe set. One pursued approach computes over-approximative solutions for linear systems; another one generates a Markov chain which approximately computes the stochastic reachable set of arbitrary dynamics.

Team AnnieWAY's Entry to the 2011 Grand Cooperative Driving Challenge

Abstract: In this paper, we present the concepts and methods developed for the autonomous vehicle known as AnnieWAY, which is our winning entry to the 2011 Grand Cooperative Driving Challenge. We describe algorithms for sensor fusion, vehicle-to-vehicle communication, and cooperative control. Furthermore, we analyze the performance of the proposed methods and compare them with those of competing teams. We close with our results from the competition and lessons learned.

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.