Abstract

When a vehicle equipped with an artificial vision system enters or exits a tunnel, the camera may temporarly suffer from reduced visibility, or even get completely blind due to quick changes in enviromental illumination.\r \r This paper presents a vision-based system that detects approaching tunnels entrances or exits. The proposed system allows other ADAS (Advanced Driver Assistance Systems) to act on camera parameters to effectively avoid the tunnel blindness effect. Information regarding approaching tunnel entrance can be helpful for other sensors as well and for sensor fusion systems. In terms of path planning, this system can also inform GNSS-based systems (Global Navigation Satellite System), which usually do not receive any signal in tunnels, and trigger dead reckoning techniques.\r \r The proposed system is noticeably fast and therefore well fit to be used as a background process to support other ADAS applications. Document type: Part of book or chapter of book

Abstract

On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions. Document type: Part of book or chapter of book

Abstract

In order to make machines perceive their external environment coherently, multiple sources of sensory information derived from several different modalities can be used (e.g. cameras, LIDAR, stereo, RGB-D, and radars). All these different sources of information can be efficiently merged to form a robust perception of the environment. Some of the mechanisms that underlie this merging of the sensor information are highlighted in this chapter, showing that depending on the type of information, different combination and integration strategies can be used and that prior knowledge are often required for interpreting the sensory signals efficiently. The notion that perception involves Bayesian inference is an increasingly popular position taken by a considerable number of researchers. Bayesian models have provided insights into many perceptual phenomena, showing that they are a valid approach to deal with real-world uncertainties and for robust classification, including classification in time-dependent problems. This chapter addresses the use of Bayesian networks applied to sensory perception in the following areas: mobile robotics, autonomous driving systems, advanced driver assistance systems, sensor fusion for object detection, and EEG-based mental states classification. Document type: Part of book or chapter of book

Abstract

This paper is focused on the fault tolerance of Human Machine Interfaces in the field of air traffic control (ATC) by accepting the overall user's body language as input. We describe ongoing work in progress in the project called Sixth Sense. Interaction patterns are reasoned from the combination of a recommendation and inference engine, the analysis of several graph database relationships and from multiple sensor raw data aggregations. Altogether, these techniques allow us to judge about different possible meanings of the current user's interaction and cognitive state. The results obtained from applying different machine learning techniques will be used to make recommendations and predictions on the user's actions. They are currently monitored and rated by a human supervisor.

Abstract

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. Document type: Article

Abstract

Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime. Document type: Article