Miguel Gavilán

Adaptive road crack detection system by pavement classification.

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Accurate Global Localization Using Visual Odometry and Digital Maps on Urban Environments

Over the past few years, advanced driver-assistance systems (ADASs) have become a key element in the research and development of intelligent transportation systems (ITSs) and particularly of intelligent vehicles. Many of these systems require accurate global localization information, which has been traditionally performed by the Global Positioning System (GPS), despite its well-known failings, particularly in urban environments. Different solutions have been attempted to bridge the gaps of GPS positioning errors, but they usually require additional expensive sensors. Vision-based algorithms have proved to be capable of tracking the position of a vehicle over long distances using only a sequence of images as input and with no prior knowledge of the environment. This paper describes a full solution to the estimation of the global position of a vehicle in a digital road map by means of visual information alone. Our solution is based on a stereo platform used to estimate the motion trajectory of the ego vehicle and a map-matching algorithm, which will correct the cumulative errors of the vision-based motion information and estimate the global position of the vehicle in a digital road map. We demonstrate our system in large-scale urban experiments reaching high accuracy in the estimation of the global position and allowing for longer GPS blackouts due to both the high accuracy of our visual odometry estimation and the correction of the cumulative error of the map-matching algorithm. Typically, challenging situations in urban environments such as nonstatic objects or illumination exceeding the dynamic range of the cameras are shown and discussed.

An Experimental Study on Pitch Compensation in Pedestrian-Protection Systems for Collision Avoidance and Mitigation

This paper describes an improved stereovision system for the anticipated detection of car-to-pedestrian accidents. An improvement of the previous versions of the pedestrian-detection system is achieved by compensation of the cameras pitch angle, since it results in higher accuracy in the location of the ground plane and more accurate depth measurements. The system has been mounted on two different prototype cars, and several real collision-avoidance and collision-mitigation experiments have been carried out in private circuits using actors and dummies, which represents one of the main contributions of this paper. Collision avoidance is carried out by means of deceleration strategies whenever the accident is avoidable. Likewise, collision mitigation is accomplished by triggering an active hood system.

3D Visual Odometry for Road Vehicles

This paper describes a method for estimating the vehicle global position in a network of roads by means of visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror of the car. Feature points are matched between pairs of frames and linked into 3D trajectories. Vehicle motion is estimated using the non-linear, photogrametric approach based on RANSAC. This iterative technique enables the formulation of a robust method that can ignore large numbers of outliers as encountered in real traffic scenes. The resulting method is defined as visual odometry and can be used in conjunction with other sensors, such as GPS, to produce accurate estimates of the vehicle global position. The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means for autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.

Free space and speed humps detection using lidar and vision for urban autonomous navigation

In this paper, a real-time free space detection system is presented using a medium-cost lidar sensor and a low cost camera. The extrinsic relationship between both sensors is obtained after an off-line calibration process. The lidar provides measurements corresponding to 4 horizontal layers with a vertical resolution of 3.2 degrees. These measurements are integrated in time according to the relative motion of the vehicle between consecutive laser scans. A special case is considered here for Spanish speed humps, since these are usually detected as an obstacle. In Spain, speed humps are directly related with raised zebra-crossings so they should have painted white stripes on them. Accordingly the conditions required to detect a speed hump are: detect a slope shape on the road and detect a zebra crossing at the same time. The first condition is evaluated using lidar sensor and the second one using the camera.

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

Real-Time Vision-Based Vehicle Detection for Rear-End Collision Mitigation Systems

This paper describes a real-time vision-based system that detects vehicles approaching from the rear in order to anticipate possible rear-end collisions. A camera mounted on the rear of the vehicle provides images which are analysed by means of computer vision techniques. The detection of candidates is carried out using the top-hat transform in combination with intensity and edge-based symmetries. The candidates are classified by using a Support Vector Machine-based classifier (SVM) with Histograms of Oriented Gradients (HOG features). Finally, the position of each vehicle is tracked using a Kalman filter and template matching techniques. The proposed system is tested using image data collected in real traffic conditions.

3D Candidate Selection Method for Pedestrian Detection on Non-Planar Roads

This paper describes a stereo-vision-based candidate selection method for pedestrian detection from a moving vehicle. Non-dense 3D maps are computed by using epipolar geometry and a robust correlation process. Non-flat road assumption is used for correcting pitch angle variations. Thus, non obstacle points can be easily removed since they lay on the road. Generic obstacles are selected by using Subtractive Clustering algorithm in a 3D space with an adaptive radius. This clustering technique can be configurable for different types of obstacles. An optimal configuration for pedestrian detection is presented in this work.

Vision-based blind spot detection using optical flow

This paper describes a vision-based system for blind spot detection in intelligent vehicle applications. A camera is mounted in the lateral mirror of a car with the intention of visually detecting cars that can not be perceived by the vehicle driver since they are located in the so-called blind spot. The detection of cars in the blind spot is carried out using computer vision techniques, based on optical flow and data clustering, as described in the following lines.

3D Visual Odometry for GPS Navigation Assistance

This paper describes a method for estimating the vehicle global position in a network of roads by means of visual odometry. To do so, the ego-motion of the vehicle relative to the road is computed using a stereo-vision system mounted next to the rear view mirror. Feature points are matched between pairs of frames and linked into 3D trajectories. The resolution of the equations of the system at each frame is carried out under the non-linear, photogrammetric approach using RANSAC. This iterative technique enables the formulation of a robust method that can ignore large numbers of outliers as encountered in real traffic scenes. The resulting method is defined as visual odometry and can be used in conjunction with other sensors, such as GPS, to produce accurate estimates of the vehicle global position. The obvious application of the method is to provide on-board driver assistance in navigation tasks, or to provide a means for autonomously navigating a vehicle. The method has been tested in real traffic conditions without using prior knowledge about the scene nor the vehicle motion. We provide examples of estimated vehicle trajectories using the proposed method and discuss the key issues for further improvement.