Special issue on car navigation and vehicle systems

Porikli, Fatih; Van Gool, Luc · 2014 · OpenAlex-citations

DOI: 10.1007/s00138-014-0603-8

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Summary

This editorial introduces a special issue of *Machine Vision and Applications* focused on car navigation and vehicle systems, edited by Fatih Porikli and Luc Van Gool. The publication addresses the critical challenges in achieving visual intelligence for autonomous driving, ranging from early self-driving experiments to modern systems like Google’s driverless cars. The editors highlight that while advanced navigation systems offer guidance and comfort, their primary role is enhancing driver assistance to ensure safety. This includes maintaining safe speeds and distances, staying within lanes, avoiding collisions with vulnerable road users, and mitigating accident severity. The editorial notes that automatic detection of objects and events remains difficult due to complex backgrounds, low-visibility weather, lighting variations, occlusions, and diverse traffic sign pictograms. The special issue comprises eleven contributions that address various aspects of visual perception and sensor integration for autonomous navigation. The first paper utilizes regional optical flow analysis from a forward-facing camera to classify traffic events with high precision. The second paper compares cameras against traditional ultrasound sensors for parking assistance, employing dense motion-stereo analysis to detect free spaces. The third paper explores multi-modal object detection, combining vision with proprioceptive sensing and LIDAR for high-integrity driving assistance. The fourth paper evaluates three active learning methods for on-road vehicle detection, focusing on recall and precision metrics. Subsequent papers address specific detection and classification tasks. The fifth paper classifies traffic events at intersections by learning normal patterns and detecting abnormalities based on accident statistics, using a fixed roadside camera. The sixth and seventh papers focus on traffic sign detection; the former uses a multi-camera system for mobile mapping and 3D localization, while the latter exploits spatiotemporal constraints from a single video stream for driving safety. The eighth paper proposes enhanced fog detection and free space segmentation using stereovision. The ninth paper demonstrates dynamic object detection through visual odometry and stereovision on embedded hardware. The tenth paper presents a low-cost hybrid architecture using optical flow to detect overtaking risks. The eleventh paper develops robust, high-throughput traffic sign detectors using center-surround HOG statistics to minimize false positives and decision time. Finally, the twelfth contribution is a survey paper that dissects road and lane detection into functional building blocks, evaluating existing methods and identifying gaps for future research. The significance of this collection lies in its comprehensive coverage of both scientifically rigorous and practically applicable solutions for autonomous navigation. By integrating diverse sensor technologies and algorithmic approaches, the papers collectively advance the field’s ability to handle the complex visual challenges inherent in real-world driving environments. The editorial underscores the importance of these contributions in bridging the gap between theoretical computer vision and practical automotive safety systems.

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