Automotive Driver Assistance Systems Research Articles

Distance Measurement Using Dual Laser Source and Image Processing Techniques

Camera based systems are gaining popularity in automotive driver assistance systems. They are used to detect obstacles, road signs, road markings and other road users. Today, even low cost camera technology enables obtaining a very good image resolution. The proposed system presented in this paper can be used to measure the distance or the displacement of an object, by applying laser optics and trigonometry. The vision-based system was developed using a dual laser source and a CCD camera for recording images which are processed to obtain the distance from an object. The image processing techniques used in this paper consists of two main improved processes which are, the detection of a laser spot using image processing and the calculation of the distance based on laser spot position on the image and the relationship between pixel number and distance. It is shown that the proposed system is able to measure distances in a quick and accurate fashion.

Vehicle recognition system based on customised HOG for automotive driver assistance system

In the last decade, advanced driver assistance systems (ADAS) made enormous progress. However, obstacle recognition tasks remain a challenge. In this paper, an optimisation vehicle detection system based on a customised histogram of oriented gradients (HOG) was presented and investigated to achieve an accurate vehicle recognition system. Our contribution in this work can be summarised in two fundamental points. First, a re-optimisation of the standard HOG parameters was made to get the best results for the car detection. Secondly, an amplification factor was distributed for each bin weight according to its contribution in the extracted car-features. Our studies using a linear support vector machine (SVM) classifier in MATLAB and heterogeneous databases of vehicle and non-vehicle images were made to achieve an excellent recognition rate that outperforms other similar approaches.

Vehicle recognition system based on customised HOG for automotive driver assistance system

Vehicle recognition system based on customised HOG for automotive driver assistance system

Research on Stereo Matching Algorithm in Intelligent Vehicle Application

Perception of environment in front of driving vehicle is a core investigation theme of intelligent vehicle technologies aiming to increase safety, convenience and efficiency of driving. Using stereo vision for environment perception is a hot technology. This paper developed an algorithm for stereo matching in intelligent vehicle application. The experimental results indicate that this algorithm is effective. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

Study on Lane Detection Based on Computer Vision

Lane detection is a crucial component of automotive driver assistance system aiming to increase safety, convenience and efficiency of driving. This paper developed a vision based algorithm of detecting road lanes which is performed by extracting edges and finding straight lines using improved Hough transform. The experimental results indicate that this algorithm is effective and precise. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

Study on Detection of Preceding Vehicles Based on Computer Vision

Detection of preceding vehicles is a critical step in automotive driver assistance systems aiming to alert a driver about driving environments and possible collision with other vehicles. This paper developed an algorithm of detecting preceding vehicles based on computer vision, which exploits synthetically the vertical edge, shadow underneath vehicle, horizontal edge and symmetry features. The experimental results indicate that this algorithm is effective and precise.

Driver Impairment Diagnostic to improve Reliability and Acceptance of Automotive Driver Assistance Systems - Application of fuzzy expert methods

Vehicle and passenger safety is a critical issue for public authorities as well as for car manufacturers and suppliers. Besides drivers training and information many technological advances have been performed since a couple of years and have drastically improved the road safety. Nevertheless the core problem remains the driver itself. Several studies held both in US and in Europe show that 90% of the accidents are due to intentional or non-intentional behaviors of the drivers, for example a bad perception or knowledge of the environment but also reduced physiological or psychological conditions. The Vehicle/Driver/Environment process can easily be compared to a closed loop system where the driver plays the role of the management/control unit. The driver pilots the vehicle considering the information it delivers, its perception of the environment and according to the targets and planning that have been set. Furthermore, the drivers behavior is influenced by its driving aptitude and capacity and should also be disturbed by environmental conditions. Despite the development of more and more sophisticated Advanced Driver Assistance Systems (ADAS) that substitute the driver in critical situations, it is currently not possible to fully exclude it from the vehicle management loop. Furthermore, the deployment of ADAS will really be efficient and accepted since it will fit with drivers needs, aptitudes and capacities to deal with complex environmental contexts, but also when providing an adapted assistance through a human centered design. Thus, an important issue is to get a better on-line knowledge about the driver and its limitations. Within this context, original functions providing an on-line driver state diagnostic about sleepiness and visual or cognitive distraction have been studied. These works are including the design and the development of complete systems from the measurement settings, up to the Human Machine Interaction concepts. A special effort was dedicated to design reliable drivers diagnostic processes. As the managed information is often imprecise or uncertain the use of qualitative reasoning methods has been foreseen. Finally, these functions and systems have been successfully implemented on several experimental vehicles and evaluated in real driving conditions.

Imaging sensor technology for intelligent vehicle active safety and driver assistant systems

Vehicular Active Safety and Driver Assistant Systems (ASDASs) rely heavily on sensors for achieving their goal of protecting the driver and passengers from potentially dangerous situations. The list of such sensors includes imaging sensors operating in different wavelength bands of the visible (i.e., video cameras) and IR spectrum, as well as ranging sensors such as ultrasonic, radar and lidar. The non–imaging ranging sensors are useful for applications that do not require object recognition/classification or scene understanding, but they generally have poor angular resolution and do not provide much information on the spatial characteristics of objects, making object recognition or classification and lane following difficult or impossible using such sensors alone. On the other hand, inexpensive vision sensors can capture the scene image in high spatial resolution and a wide field of view, which makes them ideal for object recognition and lane following under most conditions. The objective of this 'Over the Horizon' (OTH) sensor technology overview is to explore emerging imaging sensor technologies that can lead to significant capability improvement as well as cost reduction for future automotive driver assistance and active safety systems. The technologies covered include visible, IR and hyperspectral imaging systems. The authors also discuss 3D imaging systems. The authors provide a summary description of different sensors/systems, system architecture and implementation, as well as cost/performance tradeoffs, technology gaps, deployment scenarios and technology trends in the near–, mid– and long–term.

Detection and Tracking of Moving Objects for Automotive Driver Assistance System

A method derived from the adaptive Sampling Importance Resampling particle filter is proposed to solve vehicle detection and tracking problems in laser range finder data. The originality of this approach lies in a joint detection and tracking of the objects. To this end, the solution is based on a matched filter which uses a predefined vehicle model. The non-linearity of the state equations is tackled by sequential Monte Carlo methods which are here the basis of our solution. A central point here is to calculate the weights of the matched particle filter, according to the vehicle model. The proposed approach is then applied to synthetic data from a road scenario. The efficiency of the method is shown in terms of estimation accuracies and detection.

Advanced parametrical modelling of 24 GHz radar sensor IC packaging components

Abstract. This paper deals with the development of an advanced parametrical modelling concept for packaging components of a 24 GHz radar sensor IC used in automotive driver assistance systems. For fast and efficient design of packages for system-in-package modules (SiP), a simplified model for the description of parasitic electromagnetic effects within the package is desirable, as 3-D field computation becomes inefficient due to the high density of conductive elements of the various signal paths in the package. By using lumped element models for the characterization of the conductive components, a fast indication of the design's signal-quality can be gained, but so far does not offer enough flexibility to cover the whole range of geometric arrangements of signal paths in a contemporary package. This work pursues to meet the challenge of developing a flexible and fast package modelling concept by defining parametric lumped-element models for all basic signal path components, e.g. bond wires, vias, strip lines, bumps and balls.

Low‐resolution vehicle image recognition technology by frame composition of moving images

AbstractDeveloping on‐board automotive driver assistance systems intended to alert drivers about driving environments, and about possible collisions with other vehicles, has been attracting considerable attention. In particular, many researchers have suggested forward vehicle recognition technology using a camera installed in the vehicle. In forward vehicle recognition, however, it is difficult to detect the features of a vehicle from a distant vehicle image by conventional methods, because the image is too low‐resolution (LR). This paper presents a vehicle image recognition technology for detecting the features of a distant vehicle by frame composition of moving images. To detect the vehicle features from a distant LR vehicle image, we use moving images obtained from a camera installed on the vehicle, and utilize super‐resolution (SR) image reconstruction. SR image reconstruction consists of using signal processing techniques to obtain a high‐resolution (or sequence) image from observed multiple LR images. The use of this technique on real road images shows the effectiveness of the proposed techniques. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(3): 1–7, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10246

Multidimensional velocity filters for visual scene analysis in automotive driver assistance systems

Automotive scenery often contains objects that can be classified by object speed and movement direction. These features can be extracted from video data by linear n-D filters, which have already been analyzed in the past. While soundness of results was convincing, interest in those systems declined due to the reduced computational abilities of contemporary computers. Modern hardware allows realization of velocity filters, if the n-D system is carefully adapted to the analysis problem. The present paper analyzes the premises for application of velocity filters in the domain of automotive driver assistance systems, i.e. with respect to detectability of objects and implementability in a cost effective way. Especially the influence of the frame rate and the temporal violation of the sampling theorem are analyzed. Transfer functions for n-D filters working in a vision-based blind spot collision avoidance system are presented and discussed, and promising approaches for future application fields are proposed.

Millimeter-Wave-Radar Sensor Based on a Transceiver Array for Automotive Applications

This paper describes a new radar sensor architecture comprising an array of transceiver modules. Target applications of the sensor are automotive driver assistance systems. In conjunction with a monolithic integration of each transceiver, the concept offers the possibility for a cost-effective realization of digital-beamforming radar sensors at millimeter-wave frequencies. A modulation sequence is investigated based on simultaneously transmitted frequency-modulated continuous-wave signals, which are separated by frequency multiplexing. Appropriate signal processing for the estimation of range, speed, and azimuth angle in multiple object situations is presented. Experimental results with an eight-channel radar sensor in the 76-77-GHz frequency band are presented, which demonstrate the feasibility of the proposed architecture and show the performance of the modulation sequence and signal processing.

Low-Resolution Vehicle Image Recognition Technology by Frame-Composition of Moving Images

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. Especially, many researchers have suggested the forward vehicle recognition technology by a camera on vehicle. In the forward vehicle recognition, however, it is difficult to detect the features of vehicle from a distant vehicle image by conventional methods because the image is too low-resolution (LR). This paper presents vehicle image recognition technology for detecting of the features of a distant vehicle by frame-composition of moving images. To detect the vehicle features of a distant LR vehicle image, we use the moving images obtained from the camera on the vehicle, and utilize super-resolution (SR) image reconstruction. SR image reconstruction is to use signal processing techniques to obtain a high-resolution (or sequence) image from observed multiple LR images. Use of this technique on real road image, we show the effectiveness of the proposed techniques.

On-road vehicle detection: a review

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.