Imperfect in-vehicle collision avoidance warning systems can aid drivers

An experiment was conducted to determine the effects of an in-vehicle collision avoidance warning system (IVCAWS) on driver performance. A driving simulator was driven by 135 licensed drivers. Of these, 120 received alerts from the IVCAWS when their headway to a lead car was less than 2 s, and the other 15 (the control group) received no alerts. Drivers received varied alert interfaces: auditory, visual, and multimodal. The system had varied levels of reliability, determined by both false alarm rate and failure of the IVCAWS to alert to short headway. Results indicated that the IVCAWS led to safer (longer) headway maintenance. High false alarm rates induced drivers to slow down unnecessarily; large numbers of missed alerts did not have any significant impact on drivers. Driver acceptance of the system was mixed. Interface played a role in driver reliance on the system, with the multimodal interfaces generating least reliance. Actual or potential applications of this research include IVCAWS interface selection for greater system efficacy and user acceptance and the advisability of implementation, even of imperfect systems, for drivers who seek to maintain a safer headway.     

Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection

Automated airborne collision‐detection systems are a key enabling technology for facilitating the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety‐critical systems must be sensitive enough to provide timely warnings of genuine airborne collision threats, but not so sensitive as to cause excessive false alarms. Hence, an accurate characterization of detection and false‐alarm sensitivity is essential for understanding performance tradeoffs, and system designers can exploit this characterization to help achieve a desired balance in system performance. In this paper, we experimentally evaluate a sky‐region, image‐based, aircraft collision‐detection system that is based on morphological and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter background.) A novel collection methodology for collecting realistic airborne collision‐course target footage in both head‐on and tail‐chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540 m in three flight test cases with no false‐alarm events in 14.14 h of nontarget data (under cloudy conditions, the system achieved detection ranges greater than 1170 m in four flight test cases with no false‐alarm events in 6.63 h of nontarget data). Importantly, this paper is the first documented presentation of detection range versus false‐alarm curves generated from airborne target and nontarget image data. © 2012 Wiley Periodicals, Inc.     

Effects of single versus multiple warnings on driver performance

OBJECTIVE: To explore how a single master alarm system affects drivers' responses when compared with multiple, distinct warnings. BACKGROUND: Advanced driver warning systems are intended to improve safety, yet inappropriate integration may increase the complexity of driving, especially in high workload situations. This study investigated the effects of auditory alarm scheme, reliability, and collision event type on driver performance. METHOD: Using a 2 x 2 x 4 mixed factorial design, we investigated the impact of two alarm schemes (master vs. individual) and two levels of alarm reliability (high and low) on distracted drivers' performance across four collision event types (frontal collision warnings, left and right lane departure warnings, and warnings for a fast-approaching following vehicle). RESULTS: Participants' reaction times and accuracy rates were significantly affected by the type of collision event and alarm reliability. The use of individual alarms, rather than a single master alarm, did not significantly affect driving performance in terms of reaction time or response accuracy. CONCLUSION: Even though a master alarm is a relatively uninformative warning, it produced statistically no different reaction times or accuracy results when compared with information-rich auditory icons, some of which were spatially located. In addition, unreliable alarms negatively impacted driver performance, regardless of event type or alarm scheme. APPLICATION: These results have important implications for the development and implementation of multiple driver warning systems.     

基于多代理的分布式入侵检测系统模型

随着网络技术的不断发展,网络安全问题十分突出。入侵检测技术作为安全防护的重要手段,显得日益重要。大多数入侵检测系统存在误报率高和漏报的问题。此外,由于系统分布在网络的不同位置,不能进行必要的协同工作。针对上述问题,提出一种基于多代理的分布式入侵检测系统模型MADIDS。该系统在我们已有的多代理协同工作平台NHMAS上开发完成,结合基于主机的入侵检测系统和基于网络的入侵检测系统的优点,对常用入侵手段具有较高的检测率和较低的误报率。     

Lane-change detection using a computational driver model

OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.     

一种利用均值匹配改进的高光谱异常检测方法

传统高光谱异常检测算法由于背景信息估计不准确等原因普遍存在高虚警率的问题,针对这一现象,提出了一种利用图像均值进行匹配改进的高光谱异常目标检测后验处理方法。首先采用传统的高光谱异常检测算法将待检测高光谱图像划分为背景与异常目标潜在区域,之后通过对待测图像求解均值,将其与异常目标潜在区域像元进行相似性匹配计算,剔除大范围误检像元,得到最终检测结果。该方法在传统异常目标检测算法基础上进行相似度量剔除大范围虚警像元,在提高原算法探测能力的同时有效地降低虚警率。实验表明,该方法可以有效降低虚警率,提高原算法对于亚像元异常目标的检测能力,且对于不同算法、不同数据具有普适性。     

Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models

Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left- or right-lane-changing intentions by using driver models based on the queuing network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left- or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency lane changes) and low false alarm rate (0.294%).     

The influence of alarm timing on driver response to collision warning systems following system failure

This driving simulator study focuses on false and missing alarms produced by a forward collision warning system and estimates the effect of alarm timing on driver response to alarm malfunction from the perspective of driver trust in alarms. The results show that drivers who experience late alarms are reluctant to respond to a false alarm and are not influenced by a missed alarm; however, drivers who experience early alarms tend to respond to a false alarm and suffer a delayed response to critical situations when a missing alarm happens. Furthermore, drivers whose judgement of trust is relatively high, tend to exhibit delayed braking, compared with drivers that have lower levels of trust. Driver behaviour towards false and missed alarms may vary according to alarm timing and its influence on trust in alarms; moreover, impaired system effectiveness caused by alarm malfunction may be mitigated by manipulating alarm timing.     

The influence of alarm timing on driver response to collision warning systems following system failure

This driving simulator study focuses on false and missing alarms produced by a forward collision warning system and estimates the effect of alarm timing on driver response to alarm malfunction from the perspective of driver trust in alarms. The results show that drivers who experience late alarms are reluctant to respond to a false alarm and are not influenced by a missed alarm; however, drivers who experience early alarms tend to respond to a false alarm and suffer a delayed response to critical situations when a missing alarm happens. Furthermore, drivers whose judgement of trust is relatively high, tend to exhibit delayed braking, compared with drivers that have lower levels of trust. Driver behaviour towards false and missed alarms may vary according to alarm timing and its influence on trust in alarms; moreover, impaired system effectiveness caused by alarm malfunction may be mitigated by manipulating alarm timing.     

蠕虫预警及非线性传播模型优化

目前已有一些蠕虫检测系统利用蠕虫传播特性进行检测,误报率高,不能对大范围网络进行检测。为此,首先对蠕虫传播模型进行了分析和优化,提出了新蠕虫分布式传播模型。针对该模型提出了分布式蠕虫检测技术,亦即采用基于规则的检测方法监控网络蠕虫,控制台管理和协调多个检测端的工作。实验结果表明,该方法能够很好地预警蠕虫的传播行为并进行监控和报警,具有高检测率和低误报率。     

The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.     

Analysis of fault detection method based on predictive filter approach

Model-based methods are most popular in fault detection and have received consid-erable attention in the past two decades[1,2]. Based on them, many kinds of detection al-gorithms have been developed for fault detection and diagnosis[3,4]. Usually those algo-rithms are adaptive algorithms or have adaptive structures including neural networks and neuro-fuzzy networks. But the errors in the models may decrease the robustness of the methods and increase the false alarm rate. A real-time predictive…     

A novel low false alarm rate pedestrian detection framework based on single depth images

Pedestrian detection is an important image understanding problem with many potential applications. There has been little success in creating an algorithm which exhibits a high detection rate while keeping the false alarm in a relatively low rate. This paper presents a method designed to resolve this problem. The proposed method uses the Kinect or any similar type of sensors which facilitate the extraction of a distinct foreground. Then potential regions, which are candidates for the presence of human(s), are detected by employing the widely used Histogram of Oriented Gradients (HOG) technique, which performs well in terms of good detection rates but suffers from significantly high false alarm rates. Our method applies a sequence of operations to eliminate the false alarms produced by the HOG detector based on investigating the fine details of local shape information. Local shape information can be identified by efficient utilization of the edge points which, in this work, are used to formulate the so called Shape Context (SC) model. The proposed detection framework is divided in four sequential stages, with each stage aiming at refining the detection results of the previous stage. In addition, our approach employs a pre-evaluation stage to pre-screen and restrict further detection results. Extensive experimental results on the dataset created by the authors, involves 673 images collected from 11 different scenes, demonstrate that the proposed method eliminates a large percentage of the false alarms produced by the HOG pedestrian detector.     

Sorting the Wheat from the Chaff: A Study of the Detection of Alarms

The research in this paper considers the evidence on the success of alarm reduction strategies reported in the open literature. Despite strong beliefs to the contrary, the empirical evidence suggest that alarm reduction strategies have not been as successful as initially expected. This seems to be due to the fact that alarm reduction strategies actually deprive process control operators of information. In order to determine the ability of people to sift through alarm information, a study of alarm detection with three ratios of target to non-target alarms was devised (i.e. 2%, 6% and 10%) and the information was presented at three rates (i.e. 1 second, 4 seconds and 8 seconds). The results show that the ratio of target alarms has no effect on detection performance, but the temporal rate does. Given that process operators are rarely required to acknowledge alarm information in real time, it is suggested that more emphasis should be placed on initial definition of alarms and better presentation methods, rather than attempts to block the flow of alarms that have already been triggered.     

基于粒子群和人工免疫的混合入侵检测系统研究

目前,漏报率和误报率高一直是入侵检测系统（IDS）的主要问题,而IDS主要有误用型和异常型两种检测技术。根据这两种检测技术各自的优点以及它们的互补性,本文给出一种基于人工免疫的异常检测技术和基于粒子群优化（PSO）的误用检测技术相结合的IDS模型;同时,该系统还结合特征选择技术降低数据维度,提高系统检测性能。实验表明,该
系统具有较高的检测率和较低的误报率,可以自动更新规则库,并且记忆未知类型的攻击,是一种有效的检测方法。     

Alarm mistrust in automobiles: how collision alarm reliability affects driving

As roadways become more congested, there is greater potential for automobile accidents and incidents. To improve roadway safety, automobile manufacturers are now designing and incorporating collision avoidance warning systems; yet, there has been little investigation of how the reliability of alarm signals might impact driver performance. We measured driving and alarm reaction performances following alarms of various reliability levels. In Experiment One, 70 participants operated a driving simulator while being presented console emitted collision alarms that were 50%, 75%, or 100% reliable. In Experiment Two, the same participants were presented spatially generated collision alarms of the same reliability levels. The results were similar in both experiments: alarm and automobile swerving reactions were significantly better when alarms were more reliable; however, drivers still failed to avoid collisions following reliable alarms. These results emphasize that alarm designers should maximize alarm reliability while minimizing alarm invasiveness.     

分布式天线协同感知的定时捕获

为提高定时捕获的正确捕获概率并降低漏检概率,提出一种基于分布式天线协同感知的定时捕获方法。该方法针对线形小区的平坦瑞利衰落信道场景,利用两根分布式接收天线接收来自单天线移动台的发射信号;根据中心处理器处的协同感知,确保各分布式接收天线的预定虚警概率,并为门限检测推导协同处理门限;各天线根据门限检测进行分布式天线系统的最大似然定时捕获。仿真结果表明,无论移动台处于线形小区的哪个位置,提出方法在不增加预定虚警概率的情况下,均能改善各分布式天线的正确捕获概率和漏检概率。     

基于决策树和协议分析的入侵检测研究*

当前大多数入侵检测产品使用的是基于规则的简单模式匹配技术,它们存在着资源消耗量大、误报率高以及随着网速的提高出现丢包等问题。针对这些问题,提出了用决策树算法实现基于协议分析的入侵检测方法。试验结果表明,该方法具有较高的检测速度和较低的正误报率。     

基于邻域粗糙集的入侵检测

针对入侵检测系统存在的高漏报率和误报率,提出了一种基于邻域粗糙集的入侵检测方法.该方法在粗糙集理论的基础上引入邻域概念,这样便无需对数据进行离散化处理,可以减少信息损失.实验结果表明:该方法可选择出更为重要的属性组合,从而获得较高的检测率和较低的漏报率与误报率.