Vision-Based Road Detection in Automotive Systems: A Real-Time Expectation-Driven Approach

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel SIMD architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output.Comment: See http://www.jair.org/ for any accompanying file

ISR3: Communication and Data Storage for an Unmanned Ground Vehicle*

Computer vision researchers working in mobile robotics and other real-time domains are forced to con- front issues not normally addressed in the computer vision literature. Among these are communication, or how to get data from one process to another, data storage and retrieval, primarily for transient, image- based data, and database management, for maps, ob- ject models and other permanent (typically 3D) data. This paper reviews eorts at CMU, SRI and UMass to build real-time computer vision systems for mobile robotics, and presents a new tool, called ISR3, for com- munication, data storage/retrieval and database man- agement on the UMass Mobile Perception Laboratory (MPL), a NAVLAB-like autonomous vehicle

An architecture for real-time vision processing

To study the feasibility of developing an architecture for real time vision processing, a task queue server and parallel algorithms for two vision operations were designed and implemented on an i860-based Mercury Computing System 860VS array processor. The proposed architecture treats each vision function as a task or set of tasks which may be recursively divided into subtasks and processed by multiple processors coordinated by a task queue server accessible by all processors. Each idle processor subsequently fetches a task and associated data from the task queue server for processing and posts the result to shared memory for later use. Load balancing can be carried out within the processing system without the requirement for a centralized controller. The author concludes that real time vision processing cannot be achieved without both sequential and parallel vision algorithms and a good parallel vision architecture

Knowledge-based control for robot self-localization

Autonomous robot systems are being proposed for a variety of missions including the Mars rover/sample return mission. Prior to any other mission objectives being met, an autonomous robot must be able to determine its own location. This will be especially challenging because location sensors like GPS, which are available on Earth, will not be useful, nor will INS sensors because their drift is too large. Another approach to self-localization is required. In this paper, we describe a novel approach to localization by applying a problem solving methodology. The term 'problem solving' implies a computational technique based on logical representational and control steps. In this research, these steps are derived from observing experts solving localization problems. The objective is not specifically to simulate human expertise but rather to apply its techniques where appropriate for computational systems. In doing this, we describe a model for solving the problem and a system built on that model, called localization control and logic expert (LOCALE), which is a demonstration of concept for the approach and the model. The results of this work represent the first successful solution to high-level control aspects of the localization problem

Fuzzy Predictive Controller for Mobile Robot Path Tracking

IFAC Intelligent Components and Instruments for Control Applications, Annecy, France 1997This paper presents a way of implementing a Model Based Predictive Controller (MBPC) for mobile robot path-tracking. The method uses a non-linear model of mobile robot dynamics and thus allows an accurate prediction of the future trajectories. Constraints on the maximum attainable angular velocity is also considered by the algorithm. A fuzzy approach is used to implement the MBPC. The fuzzy controller has been trained using a lookup-table scheme, where the database of fuzzy-rules has been obtained automatically from a set of input-output training patterns, computed with the predictive controller. Experimental results obtained when applying the fuzzy controller to a TRC labmate mobile platform are given in the paper.Ministerio de Ciencia y Tecnología TAP95-0307Ministerio de Ciencia y Tecnología TAP96-884C

Optimized Route Network Graph as Map Reference for Autonomous Cars Operating on German Autobahn

This paper describes several optimization techniques used to create an adequate route network graph for autonomous cars as a map reference for driving on German autobahn or similar highway tracks. We have taken the Route Network Definition File Format (RNDF) specified by DARPA and identified multiple flaws of the RNDF for creating digital maps for autonomous vehicles. Thus, we introduce various enhancements to it to form a digital map graph called RNDFGraph, which is well suited to map almost any urban transportation infrastructure. We will also outline and show results of fast optimizations to reduce the graph size. The RNDFGraph has been used for path-planning and trajectory evaluation by the behavior module of our two autonomous cars “Spirit of Berlin” and “MadeInGermany”. We have especially tuned the graph to map structured high speed environments such as autobahns where we have tested autonomously hundreds of kilometers under real traffic conditions