1,616 research outputs found

    Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

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    Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we propose an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.Comment: Submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019

    Online Monitoring of Object Detection Performance During Deployment

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    During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector's performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.Comment: V2 with more experimental results and improved clarity of presentatio

    The Cowl - v.32 - n.6 - Oct 24, 1979

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    The Cowl - student newspaper of Providence College. Volume 32 - No. 6 - October 24, 1979. 12 pages

    Inventory of best practices to prevent incursions into work zones - Literature review.

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    The aim of this report was to review the literature to identify existing best practices used to prevent incursions into work zones and improve safety of road users and road workers. A systematic literature search was conducted and various available sources referred to by knowledgeable experts in the field were considered. Best practices found in the literature review comprise both regulatory/management issues and technical issues

    Evaluate the effectiveness of the speed monitoring display for work zones in Las Vegas

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    The objective of this thesis is to evaluate the performance of the SMD with different features of display sign: the size of the display sign, flashing the measured speed (with different flashing rate), and displaying warning message. In addition, this thesis will also evaluate the performance of the second SMD in a work zone. With these objectives, two testing sites, one on Cr-215 and the other on I-15, were chosen for this study. Six scenarios were developed which included (1) before condition (no SMDs in the work zone), (2) smaller sign, (3) larger sign without flashing, (4) larger sign with a fast flashing rate, (5) larger sign with a slow flashing, and (6) warning message sign. Comparisons on the speeds collected for these scenarios will be made. From the comparison among the scenarios, the effectiveness of the SMDs with different message displaying features can be evaluated. The comparisons will be made for all types of vehicles together, different types of vehicle separately, vehicles running in free flow conditions, vehicles running at excessive speeds, and vehicles operated on different lanes. From the analysis based on these comparisons, the patterns of the performance of the SMDs with different features can be better understood. After the identification of the performance patterns for the first SMD, the performance of the second SMD will be evaluated; This thesis will make contributions in the following two aspects. First, a sequential algorithm will be developed to determine whether a vehicle is running in platoon on arterials or bunching on freeways. It is important to determine these conditions because the vehicles running under these conditions would have their full liberty to make response to the tested technologies, rather than having to be influenced by vehicles running in a group. From studying the behavior of those vehicles that were not influenced by the interaction with other vehicles in a group, the identified impact of the testing technologies on them can be more revealing. In the past, to determine whether a vehicle is running in a free flow condition was conducted by using a fixed threshold on headway. This study will adopt a classification algorithm, the CUSUM algorithm, which incorporate the probability distributions of headway in free flow and platoon (or bunching) conditions. This algorithm will be calibrated based on the headway data collected at the test sites in this study. The calibrated algorithm will be operated on the headway data to determine whether a vehicle was running in platoon (bunching) or free flow conditions. By calibrating such a classification algorithm, the results from the comparisons based on free flow condition can be more reliable; The second contribution of the thesis will be developing disaggregated regression models for the probability for a vehicle to be speeding under different scenarios and the relative impact of the scenarios on the speed reduction. From these models, the effectiveness of all scenarios on reducing speed could be derived statistically; At last, the cost and benefit analysis will be provided to determine the characteristics of work zones that can receive benefits from using speed monitoring displays on arterials, like Cr-215, and freeways like I-15. Also, the recommendations for speed monitoring displays being appropriately used on arterials and freeways will be discussed. (Abstract shortened by UMI.)

    A simulation study of an autonomous steering system for on-road operation of automotive vehicles

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    The study of human driving of automotive vehicles is an important aid to the development of viable autonomous vehicle navigation techniques. Observation of human behavior during driving suggests that this activity involves two distinct levels, the conscious and the unconscious. Conscious actions relate to the logical behavior of a driver such as stopping the vehicle when a traffic light is red, slowing down the vehicle when it turns a bend, etc. Such behavior can be described using natural human language. The unconscious actions of a driver are much less obvious. There are many such activities occurring while we are driving a vehicle to a particular destination. One of the important unconscious efforts involves the selection of successive points on the road to steer the vehicle towards in order to achieve the desired road-following behavior. This research work attempts to mimic this unconscious behavior through the use of a computer simulation model. Keywords: Robotics; Artificial intelligence; Mobile; Mathematical models. (Author)Prepared for: Chief of Naval Researchhttp://archive.org/details/simulationstudyo00mcghsupported by Contract from the United States Army Combat Developments Experimentation Cente

    Human Factors Analysis of Safety Alerts in Air Traffic Control

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    Controllers receive several types of alerts from Air Traffic Control (ATC) automation systems that warn of potentially hazardous situations, including Conflict Alerts, Mode-C Intruder alerts, and Minimum Safe Altitude Warnings. This report provides a human factors analysis of ATC alerts and recommends changes to the alert algorithms and presentation that should increase controller effectiveness and overall system safety. We collected automation data from en route, approach control, and tower facilities that show how often alerts occur, how controllers respond to alerts, and when controller actions occur relative to the alerts. Of all the alerts examined, the majority received no response from controllers; many were so brief that controllers must have resolved the situation prior to the activation or the alert situation resolved itself without action by the controller. Of the alert situations where actions were taken, controllers most often took action before the alert activated. The results suggest that (a) many alerts are valid according to the alert algorithms but do not provide useful information to controllers, (b) these \u201cnuisance alerts\u201d are extremely common in the field, and (c) high nuisance alert rates may desensitize controllers and lead to poor performance. We recommend that the Federal Aviation Administration address the problem of nuisance alerts by improving safety alert algorithms, improving alert presentations, and providing better alert suppression functions. To improve safety alert algorithms, we recommend using data from this study to obtain better measures of critical reaction time parameters for alert algorithms. To reduce the impact of nuisance alerts, we recommend using alert presentations with multiple levels of urgency
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