Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible

Smart Sensor Based Obstacle Detection for High-Speed Unmanned Surface Vehicle

This paper describes an obstacle detection system for a high-speed and agile unmanned surface vehicle (USV), running at speeds up to 30m/s. The aim is a real-time and high performance obstacle detection system using both radar and vision technologies to detect obstacles within a range of 175 m. A computer vision horizon detector enables a highly accurate attitude estimation despite large and sudden vehicle accelerations. This further facilitates the reduction of sea clutter by utilising a attitude based statistical measure. Full scale sea trials show a significant increase in obstacle tracking performance using sensor fusion of radar and computer vision

Vehicle recognition and tracking using a generic multi-sensor and multi-algorithm fusion approach

International audienceThis paper tackles the problem of improving the robustness of vehicle detection for Adaptive Cruise Control (ACC) applications. Our approach is based on a multisensor and a multialgorithms data fusion for vehicle detection and recognition. Our architecture combines two sensors: a frontal camera and a laser scanner. The improvement of the robustness stems from two aspects. First, we addressed the vision-based detection by developing an original approach based on fine gradient analysis, enhanced with a genetic AdaBoost-based algorithm for vehicle recognition. Then, we use the theory of evidence as a fusion framework to combine confidence levels delivered by the algorithms in order to improve the classification 'vehicle versus non-vehicle'. The final architecture of the system is very modular, generic and flexible in that it could be used for other detection applications or using other sensors or algorithms providing the same outputs. The system was successfully implemented on a prototype vehicle and was evaluated under real conditions and over various multisensor databases and various test scenarios, illustrating very good performances

Vision based strategies for implementing Sense and Avoid capabilities onboard Unmanned Aerial Systems

Current research activities are worked out to develop fully autonomous unmanned platform systems, provided with Sense and Avoid technologies in order to achieve the access to the National Airspace System (NAS), flying with manned airplanes. The TECVOl project is set in this framework, aiming at developing an autonomous prototypal Unmanned Aerial Vehicle which performs Detect Sense and Avoid functionalities, by means of an integrated sensors package, composed by a pulsed radar and four electro-optical cameras, two visible and two Infra-Red. This project is carried out by the Italian Aerospace Research Center in collaboration with the Department of Aerospace Engineering of the University of Naples “Federico II”, which has been involved in the developing of the Obstacle Detection and IDentification system. Thus, this thesis concerns the image processing technique customized for the Sense and Avoid applications in the TECVOL project, where the EO system has an auxiliary role to radar, which is the main sensor. In particular, the panchromatic camera performs the aiding function of object detection, in order to increase accuracy and data rate performance of radar system. Therefore, the thesis describes the implemented steps to evaluate the most suitable panchromatic camera image processing technique for our applications, the test strategies adopted to study its performance and the analysis conducted to optimize it in terms of false alarms, missed detections and detection range. Finally, results from the tests will be explained, and they will demonstrate that the Electro-Optical sensor is beneficial to the overall Detect Sense and Avoid system; in fact it is able to improve upon it, in terms of object detection and tracking performance

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

Intelligent imaging systems for automotive applications

In common with many other application areas, visual signals are becoming an increasingly important information source for many automotive applications. For several years CCD cameras have been used as research tools for a range of automotive applications. Infrared cameras, RADAR and LIDAR are other types of imaging sensors that have also been widely investigated for use in cars. This paper will describe work in this field performed in C2VIP over the last decade - starting with Night Vision Systems and looking at various other Advanced Driver Assistance Systems. Emerging from this experience, we make the following observations which are crucial for "intelligent" imaging systems: 1. Careful arrangement of sensor array. 2. Dynamic-Self-Calibration. 3. Networking and processing. 4. Fusion with other imaging sensors, both at the image level and the feature level, provides much more flexibility and reliability in complex situations. We will discuss how these problems can be addressed and what are the outstanding issue