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      Driver Assistance System for Passive Multi-Trailer Vehicles with Haptic Steering Limitations on the Leading Unit

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          Abstract

          Driving vehicles with one or more passive trailers has difficulties in both forward and backward motion due to inter-unit collisions, jackknife, and lack of visibility. Consequently, advanced driver assistance systems (ADAS) for multi-trailer combinations can be beneficial to accident avoidance as well as to driver comfort. The ADAS proposed in this paper aims to prevent unsafe steering commands by means of a haptic handwheel. Furthermore, when driving in reverse, the steering-wheel and pedals can be used as if the vehicle was driven from the back of the last trailer with visual aid from a rear-view camera. This solution, which can be implemented in drive-by-wire vehicles with hitch angle sensors, profits from two methods previously developed by the authors: safe steering by applying a curvature limitation to the leading unit, and a virtual tractor concept for backward motion that includes the complex case of set-point propagation through on-axle hitches. The paper addresses system requirements and provides implementation details to tele-operate two different off- and on-axle combinations of a tracked mobile robot pulling and pushing two dissimilar trailers.

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          Most cited references 67

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          A Vision-Based Driver Nighttime Assistance and Surveillance System Based on Intelligent Image Sensing Techniques and a Heterogamous Dual-Core Embedded System Architecture

          This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.
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            A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant

            This paper proposes the Top-View Transformation Model for image coordinate transformation, which involves transforming a perspective projection image into its corresponding bird's eye vision. A fitting parameters searching algorithm estimates the parameters that are used to transform the coordinates from the source image. Using this approach, it is not necessary to provide any interior and exterior orientation parameters of the camera. The designed car parking assistant system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car. The processing time can be reduced by storing and using the transformation matrix estimated from the first image frame for a sequence of video images. The transformation matrix can be stored as the Matrix Mapping Table, and loaded into the embedded platform to perform the transformation. Experimental results show that the proposed approaches can provide a clearer and more accurate bird's eye view to the vehicle driver.
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              Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

              The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                April 2013
                03 April 2013
                : 13
                : 4
                : 4485-4498
                Affiliations
                Dpto. Ingeniería de Sistemas y Automática, Universidad de Málaga, 29071 Málaga, Spain; E-Mails: jesus.morales@ 123456uma.es (J.M.); amandow@ 123456uma.es (A.M.); ajreina@ 123456uma.es (A.J.R.); ajgarcia@ 123456uma.es (A.G.-C.)
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: jlmartinez@ 123456uma.es ; Tel.: +34-951-952-322.
                Article
                sensors-13-04485
                10.3390/s130404485
                3673095
                23552102
                20d42558-3566-464a-801e-0c6c71876241
                © 2013 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

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