Wade Bartlett

Bio: Wade Bartlett is an academic researcher from University of Rochester. The author has contributed to research in topics: Poison control & Crash. The author has an hindex of 7, co-authored 28 publications receiving 191 citations.

Evaluating the Uncertainty in Various Measurement Tasks Common to Accident Reconstruction

Abstract: When performing calculations pertaining to the analysis of motor vehicle accidents, this paper describes how investigators must often select appropriate values for a number of parameters. The uncertainty of the final answer is a function of the uncertainty of each parameter involved in the calculation. This paper presents the results of recent tests that were conducted to obtain sample distributions of some common parameters, including measurements made with tapes, measurements made with roller-wheels, skidmark measurements, yawmark measurements, estimation of crush damage from photographs, and drag factors, that can be used to evaluate the uncertainty in an accident reconstruction analysis. The paper also reviews the distributions of some pertinent data reported by other researchers.

Evaluating Uncertainty in Accident Reconstruction with Finite Differences

Abstract: The most effective allocation of accident investigation resources requires knowledge of the overall uncertainty in a set of calculations based on the uncertainty of each variable in real-world accident analyses. Many of the methods currently available are simplistic, mathematically intractable, or highly computation-intensive. This paper will present the Finite Difference method which is a numeric approach to partial differentiation with error analysis that requires no high-level mathematical ability, uses very little computation time, provides adequate results, and can be used with analysis packages of any complexity. The Finite Difference method incorporates an error treatment which provides investigators a basis to qualitatively rank from dominant to trivial the effects of uncertainty and errors in measured and estimated values. This allows for greater efforts to be placed on accident investigation and precise measurements while less effort can be spend on trivial analysis results.

Accuracy of Event Data in the 2010 and 2011 Toyota Camry During Steady State and Braking Conditions

Abstract: Independent verification of the accuracy of data from Event Data Recorders (EDRs) is useful when using the information to help reconstruct a crash. To this end, the accuracy of the EDR function of the Airbag Control Module (ACM) was tested on 2010 and 2011 Toyota Camry sedans during straight line operation. During steady state operation, and maximum ABS-braking runs starting from approximately 80 km/h (50 mph), and 113 km/h (70 mph), non-deployment events were artificially induced to store event data. Following each run, the EDR was imaged using the Bosch Crash Data Retrieval (CDR) system. The CDR reported speed values were compared to Racelogic VBox differential GPS speed records. Data recorders were also used to monitor the vehicle Controller Area Network (CAN) bus traffic, including the indicated speed, brake pressure, engine RPM, and accelerator pedal position. The speed and RPM reporting algorithms stated in CDR Data Limitations were confirmed. Exemplar graphs of EDR-reported speed/brake/RPM/accel pedal data versus GPS speed and CAN bus data are presented and discussed. The timing of the reported data with respect to the event is also discussed. The difference between vehicle speed recorded by the EDR and the GPS speed during steady state operation varied from +0.4 to −2.3 km/h, with the EDR typically reporting lower than the GPS. During heavy braking the difference in speed was observed to be from −7 to +15 km/h, with wheel slip causing negative differences, and time delay since the last CAN bus update causing positive differences. Language: en

Conducting Monte Carlo Analysis with Spreadsheet Programs

Abstract: Monte Carlo analysis has been shown to be a powerful tool in evaluating confidence limits and probability distributions for values calculated during the analysis of vehicle accidents. The use of this tool has generally required specialized software. This paper presents a method of using the tools provided with most simple spreadsheet programs to conduct Monte Carlo analysis with both evenly distributed and normally distributed variables for cases where the equations can be expressed in closed form. The paper discusses the accuracy one can expect given a particular number of trials and presents example analyses using both even-probability and normal-probability variables.

Glancing and Stopping Behavior of Motorcyclists and Car Drivers at Intersections.

Abstract: For the past decade, motorcycle fatalities have risen while other motor vehicle fatalities have declined. Many motorcycle fatalities occurred within intersections after a driver failed to see a motorcyclist. However, little is known about the behavior of motorcyclists when they negotiate an intersection. A study was undertaken to compare the behavior at intersections of an experienced group of motorcyclists when they were operating a motorcycle with their behavior when they were driving a car. Each participant navigated a course through low-volume, open roads. Participants wore eye-tracking equipment to record eye-glance information, and the motorcycle and car were instrumented with an onboard accelerometer and Global Positioning System apparatus. Results showed that participants were more likely to make last glances toward the direction of the most threatening traffic before they made a turn when they were driving a car than when they were riding a motorcycle. In addition, motorcyclists were less likely to come to a complete stop at a stop sign than car drivers. These results suggested that motorcyclists were exposing themselves to unnecessary risk. Specifically, motorcyclists frequently failed to make proper glances and practice optimal riding techniques. The behavior of the motorcyclists was compared with the current Motorcycle Safety Foundation curriculum. The results suggested that threat-response and delayed-apex techniques should be added to the training curriculum.

Fingerprinting electronic control units for vehicle intrusion detection

Abstract: As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thus-derived fingerprints are then used for constructing a baseline of ECUs' clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors - a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055%. Unlike state-of-the-art IDSs, if an attack is detected, CIDS's fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.

Error Handling of In-vehicle Networks Makes Them Vulnerable

Abstract: Contemporary vehicles are getting equipped with an increasing number of Electronic Control Units (ECUs) and wireless connectivities. Although these have enhanced vehicle safety and efficiency, they are accompanied with new vulnerabilities. In this paper, we unveil a new important vulnerability applicable to several in-vehicle networks including Control Area Network (CAN), the de facto standard in-vehicle network protocol. Specifically, we propose a new type of Denial-of-Service (DoS), called the bus-off attack, which exploits the error-handling scheme of in-vehicle networks to disconnect or shut down good/uncompromised ECUs. This is an important attack that must be thwarted, since the attack, once an ECU is compromised, is easy to be mounted on safety-critical ECUs while its prevention is very difficult. In addition to the discovery of this new vulnerability, we analyze its feasibility using actual in-vehicle network traffic, and demonstrate the attack on a CAN bus prototype as well as on two real vehicles. Based on our analysis and experimental results, we also propose and evaluate a mechanism to detect and prevent the bus-off attack.

Supporting Drivers in Keeping Safe Speed and Safe Distance: The SASPENCE Subproject Within the European Framework Programme 6 Integrating Project PReVENT

Abstract: This paper describes a novel driver-support system that helps to maintain the correct speed and headway (distance) with respect to lane curvature and other vehicles ahead. The system has been developed as part of the Integrating Project PReVENT under the European Framework Programme 6, which is named SAfe SPEed and safe distaNCE (SASPENCE). The application uses a detailed description of the situation ahead of the vehicle. Many sensors [radar, video camera, Global Positioning System (GPS) and accelerometers, digital maps, and vehicle-to-vehicle wireless local area network (WLAN) connections] are used, and state-of-the-art data fusion provides a model of the environment. The system then computes a feasible maneuver and compares it with the driver's behavior to detect possible mistakes. The warning strategies are based on this comparison. The system “talks” to the driver mainly via a haptic pedal or seat belt and “listens” to the driver mainly via the vehicle acceleration. This kind of operation, i.e., the comparison between what the system thinks is possible and what the driver appears to be doing, and the consequent dialog can be regarded as simple implementations of the rider-horse metaphor (H-metaphor). The system has been tested in several situations (driving simulator, hardware in the loop, and real road tests). Objective and subjective data have been collected, revealing good acceptance and effectiveness, particularly in awakening distracted drivers. The system intervenes only when a problem is actually detected in the headway and/or speed (approaching curves or objects) and has been shown to cause prompt reactions and significant speed correction before getting into really dangerous situations.

Evaluation of Bike Boxes at Signalized Intersections

Abstract: This paper presents a before-after study of bike boxes at 10 signalized intersections in Portland, Oregon. Before and after video were analyzed for seven intersections with green bike boxes, three intersections with uncolored bike boxes, and two control intersections. User perceptions were measured through surveys of cyclists passing through five of the bike box intersections and of motorists working downtown, where the boxes were concentrated. Both the observations and survey of motorists found a high rate of compliance and understanding of the markings. Overall, 73% of the stopping motor vehicles did not encroach at all into the bike box. Both motor vehicle and bicycle encroachment in the pedestrian crosswalk fell significantly at the bike box locations compared to the control intersections while there was mixed effects on the motorists’ encroachment in the bicycle lane. The number of observed conflicts at the bike box locations decreased even though the total number of cyclists and motor vehicles turning right increased. Observations of yielding behavior at two treatment and one control intersection found an improvement in motorists yielding to cyclists at the bike box locations. Differences in the traffic volumes and location contexts make firm conclusions about the effects of green coloring of the boxes difficult. Higher shares of surveyed motorists felt that the bike boxes made driving safer rather than more dangerous, even when the sample was narrowed to respondents who were not also cyclists. Over three-quarters of the surveyed cyclists thought that the boxes made the intersection safer.

Artificial Co-Drivers as a Universal Enabling Technology for Future Intelligent Vehicles and Transportation Systems

Abstract: This position paper introduces the concept of artificial “co-drivers” as an enabling technology for future intelligent transportation systems. In Sections I and II , the design principles of co-drivers are introduced and framed within general human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV , we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V , we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research.