Development and implementation of the UK on the spot accident data collection study - phase I

The ‘On The Spot (OTS) Accident Data Collection Study’ has been developed to overcome a number of limitations encountered in earlier and current research. Most accident studies (such as the UK Co-operative Crash Injury Study, CCIS) are entirely retrospective, in that investigations take place a matter of days after the accident and are therefore limited in scope to factors which are relatively permanent, such as vehicle deformation and occupant injuries. They do not, in general, record information relating to evidence existing at the crash site, such as post-impact locations of vehicles, weather and road surface conditions; nor do they consider events leading up to the accident, such as the driving conditions encountered as the protagonists approached the crash site and their behaviour. It is these factors which give an insight into why the accident happened. The police, who do attend the scenes of accidents while such ‘volatile’ data is still available to be collected, tend to have other priorities, such as ensuring the injured receive help, clearing the scene to restore the flow of traffic and looking for indications that any of the parties involved has broken the law. The philosophy of the OTS project was to put experienced accident researchers at the crash scene at the same time as the police and other emergency services. The study is thus still retrospective, in that the accident has already happened, but the timing is such that it should be possible to gather information on the environmental and behavioural conditions prevailing just before the crash. This provides valuable in-depth data on the causes as well as the consequences of crashes, and allows counter-measures to be developed in the fields of human behaviour and highway engineering as well as vehicle crashworthiness. This is potentially a major improvement on the data currently available from other studies. A study of this type had not been conducted in the UK for over 20 years, and comparison of the results of the current study with those of the previous one should provide interesting insights into the changes which have taken place over that period

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

A Review of Sensor Technologies for Perception in Automated Driving

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show future market trends in sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE- S3 (with grant agreement number 692455-2), by the Spanish Government through CICYT projects (TRA2015- 63708-R and TRA2016-78886-C3-1-R)

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Semantic segmentation of outdoor scenes using LIDAR cloud point

In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point clouds captured by a high definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly re-duce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and building segmentation. Our system first automatically seg-ments grounds point cloud, this is because the ground connects almost all other objects and we will use a connect component based algorithm to over segment the point clouds. Then, using binary range image processing building facades will be detected. Remained point cloud will grouped into voxels which are then transformed to super voxels. Local 3D features extracted from super voxels are classified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car. Given labeled 3D points cloud and 2D image with known viewing camera pose, the proposed association module aligned collections of 3D points to the groups of 2D image pixel to parsing 2D cubic images. One noticeable advantage of our method is the robustness to different lighting condition, shadows and city landscape. The proposed method is evaluated both quantitatively and qualitatively on a challenging fixed-position Terrestrial Laser Scanning (TLS) Velodyne data set and Mobile Laser Scanning (MLS), NAVTEQ True databases. Robust scene parsing results are reported

Laser Scanner Technology

Laser scanning technology plays an important role in the science and engineering arena. The aim of the scanning is usually to create a digital version of the object surface. Multiple scanning is sometimes performed via multiple cameras to obtain all slides of the scene under study. Usually, optical tests are used to elucidate the power of laser scanning technology in the modern industry and in the research laboratories. This book describes the recent contributions reported by laser scanning technology in different areas around the world. The main topics of laser scanning described in this volume include full body scanning, traffic management, 3D survey process, bridge monitoring, tracking of scanning, human sensing, three-dimensional modelling, glacier monitoring and digitizing heritage monuments