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Author: Marco daSilva Publisher: Createspace Independent Publishing Platform ISBN: Category : Technology & Engineering Languages : en Pages : 60
Book Description
This report presents the results of an analysis effort undertaken to address the following research question: What sensor(s) can be cost effectively added to vehicles on a wide scale to significantly improve our understanding and modeling of naturalistic near-crash/pre-crash driver performance? Current sensor and computer technology allows for the efficient collection and storage of driver and vehicle performance data on board vehicles. Crash data recorders or black boxes exist today on many vehicles though they are limited in number of recorded parameters and storage capacity. However, their capability is increasing. Recent field operational tests of advanced-technology crash avoidance systems and naturalistic driving data collection efforts have employed comprehensive data acquisition systems to characterize driver and vehicle performance as well as the driving environment. These projects gathered data on driver exposure to various environmental factors and on driver encounters with driving conflicts, near-crashes, and actual crashes. Unfortunately, the in-vehicle data acquisition packages in these projects cost over $10,000 per vehicle. It would be advantageous to build and install a very small, inexpensive package under $1,000 in a vehicle fleet of 5,000 or more. The presence of low-cost near-crash/crash event data recorders (EDRs) on thousands of vehicles would enable a more accurate assessment of safety benefits for intelligent vehicle crash avoidance technologies, and would greatly improve the quality of data in national crash databases such as the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) and General Estimates System (GES).
Author: Marco daSilva Publisher: Createspace Independent Publishing Platform ISBN: Category : Technology & Engineering Languages : en Pages : 60
Book Description
This report presents the results of an analysis effort undertaken to address the following research question: What sensor(s) can be cost effectively added to vehicles on a wide scale to significantly improve our understanding and modeling of naturalistic near-crash/pre-crash driver performance? Current sensor and computer technology allows for the efficient collection and storage of driver and vehicle performance data on board vehicles. Crash data recorders or black boxes exist today on many vehicles though they are limited in number of recorded parameters and storage capacity. However, their capability is increasing. Recent field operational tests of advanced-technology crash avoidance systems and naturalistic driving data collection efforts have employed comprehensive data acquisition systems to characterize driver and vehicle performance as well as the driving environment. These projects gathered data on driver exposure to various environmental factors and on driver encounters with driving conflicts, near-crashes, and actual crashes. Unfortunately, the in-vehicle data acquisition packages in these projects cost over $10,000 per vehicle. It would be advantageous to build and install a very small, inexpensive package under $1,000 in a vehicle fleet of 5,000 or more. The presence of low-cost near-crash/crash event data recorders (EDRs) on thousands of vehicles would enable a more accurate assessment of safety benefits for intelligent vehicle crash avoidance technologies, and would greatly improve the quality of data in national crash databases such as the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) and General Estimates System (GES).
Author: Jie Ji Publisher: Springer Nature ISBN: 303101507X Category : Technology & Engineering Languages : en Pages : 144
Book Description
In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.
Author: Vindhya Venkatraman Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Rear-end collisions are frequent yet preventable crashes in the United States. Collision warning systems have the potential to prevent crashes and mitigate crash severity. However, their success depends on the algorithms used to trigger the warnings, and computational models of rear-end collision avoidance behaviors are critical for accurate calibration of warning algorithms. This dissertation addresses gaps in driver modeling research by developing models based on three different psychological perspectives. A driving simulator experiment was used to provide data of imminent collision with a stopped or decelerating lead vehicle in the presence or absence of a warning system. Three models were developed--two were based on the information-processing approach (static models) with different parameter associations, and one was based on the ecological approach and concepts of direct perception (dynamic model). The static model with independent stages considered parameters (reaction time, jerk, and deceleration) as independent; the static model with dependent stages used copula functions to construct trivariate distributions. Associations between variables suggests that assumptions of independence are invalid. Counterfactual analysis was used to perform benefits estimation, and results show that predicted benefits of the copula-based models and those of the static models with independent stages differ by 11%. The dynamic model used perceptual variables--visual angle and expansion rate--to model onset of braking (reaction time) and deceleration profiles. This dynamic model assumes that perception occurs in the light, i.e., ecological structures relevant to collision avoidance, such as visual looming of the lead vehicle, can be used to directly specify collision-avoidance actions. The dynamic models may represent drivers' braking responses more precisely, however, traditional statistical approaches cannot be used for the parameterization of such complex models. The Approximate Bayesian Computation technique was used to parameterize these models in this dissertation. Model parameters estimated with this technique indicate that combinations of perceptual variables generate dynamic collision-imminent deceleration profiles similar to those observed in the empirical data. Between-driver variances in deceleration were captured in the perceptual variable parameters. Taken together, the different models improve our understanding of the mechanisms governing drivers' rear-end collision avoidance and provide a basis for future behavioral modeling.
Author: Christopher Armstrong Publisher: SAE International ISBN: 0768095271 Category : Technology & Engineering Languages : en Pages : 424
Book Description
Collision Reconstruction Methodologies - Volume 7B -The last ten years have seen explosive growth in the technology available to the collision analyst, changing the way reconstruction is practiced in fundamental ways. The greatest technological advances for the crash reconstruction community have come in the realms of photogrammetry and digital media analysis. The widespread use of scanning technology has facilitated the implementation of powerful new tools to digitize forensic data, create 3D models and visualize and analyze crash vehicles and environments. The introduction of unmanned aerial systems and standardization of crash data recorders to the crash reconstruction community have enhanced the ability of a crash analyst to visualize and model the components of a crash reconstruction. Because of the technological changes occurring in the industry, many SAE papers have been written to address the validation and use of new tools for collision reconstruction. Collision Reconstruction Methodologies Volumes 1-12 bring together seminal SAE technical papers surrounding advancements in the crash reconstruction field. Topics featured in the series include: • Night Vision Study and Photogrammetry • Vehicle Event Data Recorders • Motorcycle, Heavy Vehicle, Bicycle and Pedestrian Accident Reconstruction The goal is to provide the latest technologies and methodologies being introduced into collision reconstruction - appealing to crash analysts, consultants and safety engineers alike.
Author: Marco daSilva
Author: Marco daSilva
Author: C. D. Eberhard
Author: United States. Department of Transportation
Author: Jie Ji
Author: Vindhya Venkatraman
Author: Sam Talmadge
Author: 
Author: Kenneth L. Campbell
Author: C. D. Eberhard
Author: Christopher Armstrong