D. Fernandez

Night time vehicle detection for driving assistance lightbeam controller

In this paper we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will be turned on low beams when other vehicles are detected. Our systemuses a B&W micro-camera mounted in the windshield area and looking at forward of the vehicle. Digital image processing techniques are applied to analyze light sources and to detect vehicles in the images. The algorithm is efficient and able to run in real-time. Some experimental results and conclusions are presented.

Pedestrian Detection Using SVM and Multi-Feature Combination

This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in the framework of intelligent transportation systems. The basic components of pedestrians are first located in the image and then combined with a SVM-based classifier. This poses the problem of pedestrian detection in real, cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A by-components learning approach is proposed in order to better deal with pedestrians variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods, as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes, either at daytime and nighttime. The results achieved up to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance

Vision-based adaptive cruise control for intelligent road vehicles

There is a broad range of robotics technologies that are currently being applied to the generic topic of intelligent transportation systems (ITS). One of the most important research topics in this field is adaptive cruise control (ACC), aiming at adapting the vehicle speed to a predefined value while keeping a safe gap with regard to potential obstacles. For this purpose, a monocular vision system provides the distance between the ego vehicle and the preceding vehicle on the road. The complete system can be understood as a vision-based ACC controller, based on fuzzy logic, which assists the velocity vehicle control offering driving strategies and actuation over the throttle of a car. This controller is embedded in an automatic driving system installed in two testbed mass-produced cars operating in a real environment. The results obtained in these experiments show a very good performance of the vision-based gap controller, which is adaptable to all speeds and safe gap selections.

Road Vehicle Recognition in Monocular Images

This paper describes a monocular vision-based Vehicle Recognition System in which the basic components of road vehicles are first located in the image and then combined with a SVM-based classifier. The challenge is to use a single camera as input. This poses the problem of vehicle detection and recognition in real, cluttered road images. A distributed learning approach is proposed in order to better deal with vehicle variability, illumination conditions, partial occlusions and rotations. The vehicle searching area in the image is constrained to the limits of the lanes, which are determined by the road lane markings. By doing so, the rate of false positive detections is largely decreased. A large database containing thousands of vehicle examples extracted from real road images has been created for learning purposes. We present and discuss the results achieved up to date.

XPFCP: An Extended Particle Filter for Tracking Multiple and Dynamic Objects in Complex Environments

The work presented in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An extended particle filter (XPF) is used to implement a multimodal distribution that will represent the most probable estimation for each object position. A standard particle filter (PF) cannot be used with a variable number of obstacles, and some other solutions have been tested in different previous works, but most of them are very expensive in time and memory resources at least for a high number of obstacles to be tracked. The solution exposed here includes a clustering procedure that increases the robustness of the probabilistic process to adapt itself on-line to the variable number of clusters. The presented algorithm has been tested with sonar and stereovision measurements and some results included in the paper show the efficiency of the proposed work.

AUTOPIA architecture for automatic driving and maneuvering

Cybercars and dual mode vehicles are presently the most innovative testbeds for vehicular automation applications. The definition of standards and control architectures of the different automatic vehicle onboard systems is a necessary task to build a final prototype to be produced. Several classical architecture definitions have been made in the field of mobile robotics. These architectures are capable of dealing with sensorial inputs and environment and procedural knowledge to manage the different actuators of mobile robots in order to accomplish their missions. Autonomous vehicles are conceived as a link between mobile robotics and the field of vehicular technology, obtaining cars that may be as autonomous as a mobile robot but circulating in high demand environments and in different conditions, as compared to robots. In this paper we present the control architecture used in AUTOPIA program, used for automating mass produced cars. This architecture is to deal with sensorial information and wireless communication as main sensorial input and manages the three fundamental actuators in a car: throttle, brake and steering wheel. The final aim of this architecture is to cover an automatic driving system that can manage a set of maneuvers of a car in the same way human drivers do. At this moment, straight circulation, curve circulation, adaptive cruise control, stop and go and overtaking maneuvers are available and research continues in order to increment its number

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.

XPFCP: an extended particle filter for tracking multiple and dynamic objects in complex environments

The work described in this paper explores a new solution for tracking multiple and dynamic objects in complex environments. An XPF (extended particle filter) is used to implement a multimodal distribution that represents the most probable estimation for each object position and velocity. A standard PF (particle filter) cannot be used with a variable number of obstacles; some other solutions have been tested in different previous works, but most of them require heavy computational resources at least for a high number of obstacles to be tracked. The solution described here includes a clustering procedure that increases the robustness of the probabilistic process in order to provide on-line adaptation to the variable number of clusters. The result is the XPFCP: extended particle filter with clustering process. The presented algorithm has been tested using stereovision measurements; the results included in the paper show the efficiency of the proposed system.

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.