Error Analysis in a Stereo Vision-Based Pedestrian Detection Sensor for Collision Avoidance Applications

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance

Traffic jam driving with NMV avoidance

n recent years, the development of advanced driver assistance systems (ADAS) – mainly based on lidar and cameras – has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations—in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuators – brake and throttle pedals – were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging

Automated Visual Database Creation For A Ground Vehicle Simulator

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter

Synthesis of Best Practice for Increasing Protection and Visibility of Highway Maintenance Vehicles, CTRE Final Report Iowa DOT Project TR-475, August 2002

The purpose of this research project is to study current practices in enhancing visibility and protection of highway maintenance vehicles involved in moving operations such as snow removal and shoulder operations, crack sealing, and pothole patching. The results will enable the maintenance staff to adequately assess the applicability and impact of each strategy to their use and budget. The report’s literature review chapter examines the use of maintenance vehicle warning lights, retroreflective tapes, shadow vehicles and truck-mounted attenuators, and advanced vehicle control systems, as well as other practices to improve visibility for both snowplow operators and vehicles. The chapter concludes that the Manual on Uniform Traffic Control Devices does not specify what color or kind of warning lights to use. Thus, a wide variety of lights are being used on maintenance vehicles. The study of the relevant literatures also suggests that there are no clear guidelines for moving work zones at this time. Two types of surveys were conducted to determine current practices to improve visibility and safety in moving work zones across the country and in the state of Iowa. In the first survey of state departments of transportation, most indicated using amber warning lights on their maintenance vehicles. Almost all the responding states indicated using some form of reflective material on their vehicles to make them more visible. Most participating states indicated that the color of their vehicles is orange. Most states indicated using more warning lights on snow removal vehicles than their other maintenance vehicles. All responding state agencies indicated using shadow vehicles and/or truck-mounted attenuators during their moving operations. In the second survey of Iowa counties, most indicated using very similar traffic control and warning devices during their granular road maintenance and snow removal operations. Mounting warning signs and rotating or strobe lights on the rear of maintenance vehicles is common for Iowa counties. The most common warning devices used during the counties’ snow removal operations are reflective tapes, warning flags, strobe lights, and auxiliary headlamps

Online Mapping-Based Navigation System for Wheeled Mobile Robot in Road Following and Roundabout

A road mapping and feature extraction for mobile robot navigation in road roundabout and road following environments is presented in this chapter. In this work, the online mapping of mobile robot employing the utilization of sensor fusion technique is used to extract the road characteristics that will be used with path planning algorithm to enable the robot to move from a certain start position to predetermined goal, such as road curbs, road borders, and roundabout. The sensor fusion is performed using many sensors, namely, laser range finder, camera, and odometry, which are combined on a new wheeled mobile robot prototype to determine the best optimum path of the robot and localize it within its environments. The local maps are developed using an image’s preprocessing and processing algorithms and an artificial threshold of LRF signal processing to recognize the road environment parameters such as road curbs, width, and roundabout. The path planning in the road environments is accomplished using a novel approach so called Laser Simulator to find the trajectory in the local maps developed by sensor fusion. Results show the capability of the wheeled mobile robot to effectively recognize the road environments, build a local mapping, and find the path in both road following and roundabout

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

Realization of Performance Advancements for WPI\u27s UGV - Prometheus

The objective of this project is to design and implement performance improvements for WPI\u27s intelligent ground vehicle, Prometheus, leading to a more competitive entry at the Intelligent Ground Vehicle Competition. Performance enhancements implemented by the project team include a new upper chassis design, a reconfigurable camera mount, extended Kalman filter-based localization with a GPS receiver and a compass module, a lane detection algorithm, and a modular software framework. As a result, Prometheus has improved autonomy, accessibility, robustness, reliability, and usability

Mobility and Aging: Older Drivers’ Visual Searching, Lane Keeping and Coordination

This thesis examined older drivers’ mobility and behaviour through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behaviour and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, GNSS tracking, and GIS. Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers

Aerodrome situational awareness of unmanned aircraft: an integrated self-learning approach with Bayesian network semantic segmentation

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This paper investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the United Kingdom. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction and unknown obstacles, we develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis for the outdoor experimental data shows that the integrated method developed in this paper improves robustness of situational awareness for autonomous taxiing