This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

/pdf/gold-a-parallel-real-time-stereo-vision-system-for-generic-3mpgpjb6f8.pdf

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

https://www.cbica.upenn.edu/sbia/papers/384.pdf

Lane detection and tracking using B-Snake

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

https://www.tuc.gr/fileadmin/users_data/elci/Kalaitzakis/J.25.pdf

A survey of video processing techniques for traffic applications

A vehicular vision system includes a camera having a lens and a CMOS photosensor array having a plurality of photosensor elements. The camera is disposed at an interior portion of a vehicle equipped with the vehicular vision system. The camera one of (i) views exterior of the equipped vehicle through the windshield of the equipped vehicle and forward of the equipped vehicle and (ii) views from the windshield of the equipped vehicle into the interior cabin of the equipped vehicle. A control includes an image processor that processes image data captured by the photosensor array. The image processor processes captured image data to detect an object viewed by the camera. The photosensor array is operable at a plurality of exposure periods and at least one exposure period of the plurality of exposure periods is dynamically variable.

Vehicular vision system

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.

/pdf/vision-based-road-detection-in-automotive-systems-a-real-1g2lut9umh.pdf

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.

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

This work presents a Computer Vision system for road boundary detection in automotive applications. Images are processed by a multiresolution algorithm, driven by a-priori knowledge through a top-down control. In order to face the hard real-time constraints of automotive tasks, a special purpose massively parallel computer architecture, PAPRICA, has been developed. The whole system is currently operative on MOB-LAB mobile laboratory: a land vehicle integrating the results of the activities of the Italian PROMETHEUS units. The basis of the algorithm is discussed using the formal tools of mathematical morphology, while the choice of the computing architecture and of the computational paradigm is explained. The generality of the presented approach allows its use also to solve similar problems, namely to detect features exploiting a long-distance correlation, such as the road boundaries in vehicular applications. >

S. Berte

Papers

Vision-based road detection in automotive systems: a real-time expectation-driven approach

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

A morphological model-driven approach to real-time road boundary detection for vision-based automotive systems