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28 Figure 4.3. DART developed by VTTI. registered users. Acquired data sets go through rigorous data factory to make a judgment about nonrecurring congestion quality assurance and validation procedures and can be refor- caused by driver behavior as long as the equipment can matted for use in standard analytical systems (e.g., ADAAS, (1) secure the data so that only authorized personnel can access SAS, and SPSS) and on multiple software platforms. Two it; (2) be large enough to accommodate the video data, radar servers hosted UMTRI data when it was first collected in 2004 data, and associated vehicle and external data; (3) organize a through 2005. The first server is a Dell PowerEdge 4600 with a relational database so that the data can be queried and retrieved 12-GB system drive, a 546-GB RAID 10 drive containing data- efficiently; and (4) allow synchronized playback of the video base data files, and a 124-GB RAID 1 drive containing database data together with other vehicle and external data. log files. The server runs Windows 2003 Server SP2 and Microsoft SQL Server 2005. The video files are stored on a sep- Data Reduction and Crash and arate Dell PowerEdge 4600 with a 1.2-TB logical drive. The Near-Crash Detection servers are among several servers housing data from six FOTs and dozens of other projects that are located in a small, secure As stated, data collection studies that include video data usually server room. UMTRI uses data structures that are intended to generate large files. The 100-Car Study raw data set, including provide fast access so that custom tools allow users to access both video and vehicle kinematic data, requires 7 TB of data video in any project within 5 s. Direct access to the data and storage. Reduced data sets require much less storage capacity. video is available for authorized users located within UMTRI, The quality of reduced data sets should be sufficient for the throughout the university, and within selected research part- purposes of this study. ners, including access to all tools from remote locations. With the exception of the NTNDS, which is still involved The descriptions provided summarize the data computation in an ongoing data reduction effort (see Table 3.7), the other and storage abilities of VTTI and UMTRI. Data-processing three VTTI data sets and the two UMTRI FOT studies have capability does not have to achieve such levels to conduct video been reduced and are discussed in detail in this chapter. The datarelated research. Data storage and reduction will be satis- data reduction methodology, criteria to identify crashes and

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29 near crashes, and the associated weather and traffic condi- exceed 2 m/s. Specifically, the weather was coded as 0 for "dry," tions at the point that crashes and near crashes occurred are 1 for "wet," and 2 for "snow covered." As defined in the data enumerated. reduction dictionary, the road was classified as wet if it was wet from snow but not snow covered. Also, any moisture on the road was classified as wet; there did not need to be standing Project 2: Automotive Collision Avoidance water. A snow-covered classification would have included ice- System Field Operational Test covered if it was observed. If any portion of the road, including The original purpose of the study was to test the effectiveness of turn lanes, were covered in snow, the classification was snow the FCW and ACC systems developed and integrated by GM covered. The traffic density was coded using the number of vis- and Delphi. The FCW delivers a warning when there is a for- ible traffic counts in the front camera, as shown in Figure 4.5 ward crash possibility, and the ACC system maintains a safe (1), in which "sparse" describes the top figure for which headway. In addition to these systems, a DAS developed by smoothed traffic count is less than 1.5 targets, "moderate" UMTRI was installed on the test vehicles. The DAS deployed in describes the middle figure for which smoothed traffic count is this study organized data by trips. The system started to collect between 1.5 and 4.0 targets, and "dense" describes the bottom data each time the vehicle ignition was turned on and stopped figure for which smoothed traffic count is greater than 4.0 tar- when the engine was stopped. After each trip, files comprising gets. The reduced data dictionary appears in Appendix A (1). the time history, transition, trip summary, triggered summary, Although the reduced data set included a description of raw CAN, video (each record contains a time and the bitmap secondary tasks (e.g., cell phone use, eating, drinking, smok- image), and audio were recorded in the DAS. ing, and grooming), hand location, and eye location, it did The video system had two cameras: a forward camera and a not specify if the behavior was a contributing factor to the face camera. The forward scene was recorded continuously at event. The team explored the possibility of conducting addi- 1 Hz, and the driver's face was sampled at 5 Hz for 4 s every tional data reduction to identify contributing factors but real- 5 min. Exposure video was saved simultaneously. One of the ized that the RDCWS FOT conducted by UMTRI (Project 5) following triggers enabled the video system to capture a retro- has a similar but more sophisticated data structure compared spective clip, 8 s of video data (5 s before the event and 3 s after with that for this project; that data set fits better with the cur- for the forward camera; 4 s before and 4 s after for the face rent research goal. Also, the data transferring of this project camera), and saved the data to disk. These were activated by has issues because of a waiver signed by the participants to (1) a driver comment button; (2) Conventional Cruise Con- allow for secondary usage of the data. To complete the addi- trol (CCC) and ACC engagement events; (3) FCW events; and tional data reduction for the UMTRI studies in a timely man- (4) hard-braking events. ner, it was decided that the team would focus on the data The files recorded by the triggers were sent to UMTRI by collected from Project 5, and the results of data reduction way of cell modem for the purpose of preparing and facilitat- performed by the team are discussed in Chapter 8. ing the participant debriefing. When the car was returned to UMTRI, software was used to process and view data, as shown Project 5: Road Departure Crash Warning in Figure 4.4 (1). Correspondingly, there were two phases of System Field Operational Test data reduction. The first phase was completed by the DAS as the vehicles were being driven by the participants. The onboard The research goal was to study the effectiveness and acceptance data-processing system generated CAN message lists and his- of the RDCWS, in which a Lane Departure Warning System tograms for multiple variables, as well as an inventory file with (LDWS), a Curve Speed Warning System (CSWS), and a DAS all triggers. The second phase involved the processing of data by were equipped onboard to provide warnings and collect data. analysts after the vehicle was returned to UMTRI. The resulting The LDWS used a camera to observe visual features that delin- data set of the ACAS FOT contained approximately 164 GB of eate lane and road edges and a set of onboard radar units to data. Invalid trips were filtered out by discarding trips with modulate the warnings when potentially dangerous objects any of the following attributes: zero distance; maximum speed were sensed. The CSWS used GPS technology and a digital less than 20 mph; system health histogram not okay; critical map to anticipate curve location and radius. The CSWS issued okay less than 90%; frozen threat index; radar malfunction; or an alert when the speed was detected to be unsafe for the curve. non-FOT subject driving. The DAS, developed and managed by UMTRI, collected While the video data were examined, environmental fac- data from the RDCWS CAN bus, two radar buses, two video tors (e.g., weather and traffic density), target characteristics, streams, an audio stream, and several other instruments. Data driver behavior, alert classification, and the driving scenario variables collected in this study are listed in Table 4.1 (2). were identified and classified. A total of 634 alerts were man- The two video cameras--one capturing the driver's face ually examined wherever the target speed was estimated to and the other recording the forward view--recorded video

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30 Figure 4.4. Software interface used in Project 2. data at the frequencies shown in Table 4.2. Specifically, the To reduce the data, analysts assigned values to 24 variables DAS captured a 5-s video clip every 5 min from the face based on a review of the video and numerical data from the camera, regardless of the driving situations or driver activ- alerts. Twelve of the variables were associated with the cir- ity, creating a random sample of driver activity. The triggers cumstances (e.g., road types and curves), and 12 variables (RDCWS alerts generated by the LDWS, CSWS, or a driver addressing driver activities before, during, and after the alert comment button pressed by the driver) sent a signal to the (e.g., driver's distractions, including glance direction) were video system, and the cameras then recorded data at a higher coded for the events. Appendix B contains a summary of the frequency (as shown in Table 4.2) for 4 s before the event and data dictionary (2), and Figure 4.6 shows a screenshot of the 4 s after the alert. video data collected in this project. Although the frequency

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31 Figure 4.5. Traffic density definitions in data reduction in Project 2. Table 4.1. Data Collected in Project 5 Data Sources Vehicle and driver identifications Preset values Vehicle position, heading, and motion: speed, yaw rate, accelerations, pitch and roll angle and CAN bus and FOT sensors rates, GPS (differential and nondifferential) Driver control inputs: steering wheel angle, throttle input, brake switch, turn signal, headlamp CAN bus state, cruise control state and set speeds, LDWS and CSWS sensitivity settings RDCWS driver displays: LDWS and CSWS alerts and levels, availability icons CAN bus RDCWS intermediate values: lane position, warning thresholds, road geometry estimates, threat CAN bus locations, vehicle-centered object map Roadway environment: road type and attributes, urban and rural settings Onboard digital map via CAN bus, plus postprocessing, Highway Performance Monitoring System (HPMS) database RDCWS and subsystem health and diagnostics information, as well as subsystem version numbers CAN bus RDCWS radar data: forward radar data, side radar data CAN bus Video: forward driving scene and driver face views LDWS camera, FOT sensors Audio from driver comment button: dictated messages from driver FOT sensors

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32 Table 4.2. Video Camera Configurations in Project 5 Nominal Triggered Rate Rate Pretrigger Posttrigger Video (Hz) (Hz) Window(s) Window(s) Forward video 2 10 4 4 Face video 0.5 10 4 4 Face video--exposure 5 n/a 0 5 set for the video cameras was relatively low, the data collected in conflicts were "any event that increases the level of risk asso- this study are accurate, comprehensive, and efficiently reduced. ciated with driving but does not result in a crash, near-crash, or incident. . . . Examples include: driver control error with- out proximal hazards being present; driver judgment error, Project 6: 100-Car Study such as unsafe tailgating or excessive speed; or cases in which The data reduction performed in this project can be summa- drivers are visually distracted to an unsafe level." Proximity rized in two steps. First, events were identified using predefined events were "any circumstance resulting in extraordinarily trigger criteria values that resulted in a low miss rate and a high close proximity of the subject vehicle to any other vehicle, false alarm rate to avoid missing valid events. These criteria were pedestrian, cyclist, animal, or fixed object where, due to derived after a sensitivity analysis. The specifics of the trigger apparent unawareness on the part of the driver(s), pedestri- values are described in Table 4.3. ans, cyclists or animals, there is no avoidance maneuver or Second, the video data for all the events identified were response. Extraordinarily close proximity is defined as a clear reviewed by data reductionists. The reductionists focused on a case where the absence of an avoidance maneuver or response 90-s epoch for each event (60 s before and 30 s after the event) is inappropriate for the driving circumstances (including to validate the event, determine severity, and code the event for speed, sight distance, etc.)." Crash-relevant events were "any a data reduction dictionary. circumstance that requires a crash avoidance response on the The severities of the valid events were determined based on part of the subject vehicle or any other vehicle, pedestrian, various criteria, and all required variables were recorded and cyclist, or animal that is less severe than a rapid evasive maneu- edited in MySQL databases. According to the severities, the ver, but greater in severity than a `normal maneuver' to avoid valid events were classified into five categories: nonconflict, a crash. A crash avoidance response can include braking, proximity events, crash-relevant, near crash, and crash. Non- steering, accelerating, or any combination of control inputs. Figure 4.6. Video data collected in Project 5.

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33 Table 4.3. Event Triggers in Project 6 Trigger Type Description Lateral acceleration Lateral motion equal to or greater than 0.7 g. Longitudinal acceleration Acceleration or deceleration equal to or greater than 0.6 g. Acceleration or deceleration equal to or greater than 0.5 g coupled with a forward TTC of 4 s or less. All longitudinal decelerations between 0.4 and 0.5 g coupled with a forward TTC value less than or equal to 4 s; corresponding forward range value at the minimum TTC is not greater than 100 ft. Event button Activated by the driver pressing a button on the dashboard when an event occurred that he or she deemed critical. Forward TTC Acceleration or deceleration equal to or greater than 0.5 g coupled with a forward TTC of 4 s or less. All longitudinal decelerations between 0.4 and 0.5 g coupled with a forward TTC value less than or equal to 4 s; corresponding forward range value at the minimum TTC is not greater than 100 ft. Rear TTC Any rear TTC trigger value of 2 s or less that also has a corresponding rear range distance less than or equal to 50 ft and any rear TTC trigger value in which the absolute acceleration of the following vehicle is greater than 0.3 g. Yaw rate Any value greater than or equal to a plus and minus 4-degree change in heading (i.e., vehicle must return to the same general direction of travel) within a 3-s window. A `normal maneuver' for the subject vehicle is defined as a tutes a rapid maneuver." Crashes were defined as events with control input that falls inside of the 99 percent confidence "any contact with an object, either moving or fixed, at any limit for control input as measured for the same subject." Near speed, in which kinetic energy is measurably transferred or crashes were defined as "any circumstance that requires a dissipated. Includes other vehicles, roadside barriers, objects rapid, evasive maneuver by the subject vehicle, or any other on or off the roadway, pedestrians, cyclists or animals" (34). vehicle, pedestrian, cyclist, or animal to avoid a crash. A rapid, The variables defined in the reduced data can be catego- evasive maneuver is defined as a steering, braking, accelerat- rized into one of the following homogeneous groups: general ing, or any combination of control inputs that approaches the information, event variables, contributing factors, surround- limits of the vehicle capabilities. As a guide: subject vehicle ing factors, driver state variables, and driver information of braking greater than 0.5 g, or steering input that results in a the second vehicle involved. The variables are described in lateral acceleration greater than 0.4 g to avoid a crash, consti- Table 4.4. Table 4.4. List of Variables in Reduced Data in Project 6 Classification List of Variables General information Vehicle number, epoch number, event severity, trigger type, driver subject number, onset of precipitating factor, and resolution of the event Event variables Event nature, incident type, pre-event maneuver, judgment of Vehicle 1 maneuver before event, precipitating fac- tor, evasive maneuver, and vehicle control after corrective action Contributing factors Driver behavior: Driver 1 actions and factors relating to the event, Driver 1 physical or mental impairment, Driver 1 distracted, willful behavior, driver proficiency, Driver 1 drowsiness rating, Driver 1 vision obscured by, and vehicle contributing factors Environmental factors: Weather, light, windshield wiper activation, surface condition, and traffic density (level of service) driving environment Driving environment: Kind of locality, relation to junction, traffic flow, number of travel lanes, traffic control, and alignment infrastructure Driver state variable Driver 1 hands on wheel, occupant safety belt usage, Driver 1 alcohol use, fault assignment, average PERCLOS, and Driver 1 eyeglance reconstruction Driver/Vehicle 2 Number of other vehicles/person(s), location of other vehicle(s)/persons, Vehicle/Person 2 type, Vehicle 2 maneu- ver, Driver/Vehicle 2 corrective action attempted, Driver/Vehicle 2 physical or mental impairment, and Driver 2 actions and factors relating to crash or incident

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34 As listed in Table 4.4, event nature was one of the variables Conflict with a vehicle moving across the subject vehicle coded in the reduced data set. Event nature usually was path through the intersection; decided by reductionists based on predefined criteria together Conflict with a parked vehicle; with subjective judgments. According to the definition, the Conflict with a pedestrian; valid events were classified into five categories according to Conflict with a pedal cyclist; their severity: nonconflict, proximity event, crash-relevant, Conflict with an animal; near crash, and crash. These categories are described previously Conflict with an obstacle or object in the roadway; in this report. Single-vehicle conflict; Among the safety-related events, crashes are relatively easy Other (specify); and to identify. Near crashes and crash-relevant events are not as Unknown conflict. straightforward. Many safety applications would greatly ben- efit from a reliable, purely quantitative definition of a near Table 4.5 shows the level of service (LOS) at the time the crash and crash-relevant event. Because a near crash is more crash or near crash happened. Preferably, the LOS both severe than a crash-relevant event and could have developed before and after the event can be identified so that the travel into a real crash, it is important to differentiate a near crash time reliability caused by incidents can be analyzed and mod- from a crash-relevant event. During data reduction, it was eled. (Details of modeling travel time reliability are discussed found that strictly applying a fixed number as a threshold in Chapter 6.) Once a car was involved in an accident and the value in acceleration or speed to identify near crashes inher- engine was turned off, the onboard data collection system ently generates some "noise." For example, a TTC of 0.1 s would be turned off and would cease collecting data. There- occurs regularly on interstates during congestion and is nor- fore, the LOS after events usually was not available. Table 4.5 mal, whereas the same TTC happening on a rural road at lists only the LOS before the events occurred. night is significantly different. Also, because of the varied The weather conditions associated with safety-related driving habits of different drivers, it is hard to apply one uni- events varied. Clear weather was associated with the same form number. Some drivers apply more frequent aggressive percentage of 100-Car Study crashes (78%) and near crashes brakes simply because they are comfortable with rapid decel- (78%). The second most associated weather factor was rain. erations. Therefore, both qualitative and quantitative criteria Drivers had a slightly higher percentage of crashes (12%) than should be incorporated. As a guideline in this study, a subject near crashes (8%) that occurred during rainy days. Cloudy vehicle braking greater than 0.5 g or a steering input that weather was associated with more near crashes (13%) than results in a lateral acceleration greater than 0.4 g to avoid a crashes (9%). Only one 100-Car Study crash occurred with crash constitutes a candidate rapid maneuver, as defined in snow as an associated factor. near crashes. Combined with the subjective judgment of experienced reductionists, this maneuver is decided by reduc- Project 7: Drowsy Driver Warning System tionists as a near crash or crash-relevant event. A similar pol- Field Operational Test icy for identifying near crashes was applied to most VTTI studies with appropriate adjustments made for threshold val- Data reduction started with the identification of potential ues. Altogether, 69 crashes and 761 near crashes were identi- events using the DART. A 90-s epoch was created for each fied in the 100-Car data set. The crashes and near crashes event, which included 1 min before the trigger and 30 s after. were parsed into the following 18 conflict categories: The automatic scanning resulted in an event database. The triggers and values used in identifying critical events are listed Conflict with a lead vehicle; in Table 4.6. Conflict with a following vehicle; Data reductionists reviewed the video data for the identi- Conflict with oncoming traffic; fied events to validate them. Invalid events for which sensor Conflict with a vehicle in an adjacent lane; readings were spurious because of a transient spike or some Conflict with a merging vehicle; other false-positive classification were filtered out. Valid Conflict with a vehicle turning across the subject vehicle events were classified as conflicts or nonconflicts. Conflicts path in the same direction; were further classified into four categories based on the sever- Conflict with a vehicle turning across the subject vehicle ity: crash, crash: tire strike, near crash, and crash-relevant. Non- path in the opposite direction; conflicts were events with valid threshold values but did not Conflict with a vehicle turning into the subject vehicle path create safety-significant traffic events. Verified valid conflicts in the same direction; were categorized based on the following descriptions. Crashes Conflict with a vehicle turning into the subject vehicle path are classified as any contact with an object, either moving or in the opposite direction; fixed, at any speed in which kinetic energy is measurably

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35 Table 4.5. LOS for Crashes and Near Crashes by Conflict Type Crashes Near Crashes Conflict type LOS A LOS B LOS C+ LOS A LOS B LOS C+ Single 24 20 3 1 48 42 3 3 Lead 15 5 5 5 380 78 241 61 Following 12 4 7 1 70 16 39 15 Obstacle 9 6 3 0 6 1 5 0 Animal 2 2 0 0 10 10 0 0 Turning across opposite direction 2 1 1 0 27 12 15 0 Adjacent vehicle 1 0 1 0 115 32 69 14 Parking 4 3 1 0 5 3 2 0 Across path through intersection NA 27 11 15 1 Oncoming 27 14 12 1 Other 2 1 1 0 Pedestrian 6 4 1 1 Turning across in same direction 3 2 1 0 Turning in same direction 28 16 12 0 Merging 6 2 4 0 Unknown 1 1 0 0 transferred or dissipated. Near crashes (evasive maneuver) vehicle to any other object. Extraordinary proximity is defined are classified as any circumstance that requires a rapid, eva- as a clear case in which the absence of an avoidance maneuver sive maneuver, including steering, braking, accelerating, or or response is inappropriate for the driving circumstances any combination of control inputs that approaches the limits (e.g., speed and sight distance). Crash-relevant conflicts (eva- of the vehicle capabilities, by the subject vehicle or any other sive maneuvers) are assessed similar to near crashes (evasive vehicle, pedestrian, cyclist, or animal to avoid a crash. Near maneuvers). Longitudinal decelerations of -0.35 g or greater are crashes (no evasive maneuver) are classified as any circum- reviewed to assess whether they qualify as crash-relevant con- stance that results in extraordinary proximity of the subject flicts (or near crashes); those with decelerations of -0.50 g or Table 4.6. Trigger Values to Identify Critical Incidents in Project 7 Trigger Type Description Longitudinal acceleration (LA) (1) Acceleration or deceleration greater than or equal to 0.35 g . Speed greater than or equal to 15 mph. (2) Acceleration or deceleration greater than or equal to 0.5 g . Speed less than or equal to 15 mph. Time to collision (TTC) (3) A forward TTC value of less than or equal to 1.8 s, coupled with a range of less than or equal to 150 ft, a target speed of greater than or equal to 5 mph, a yaw rate of less than or equal to 4/s , and an azimuth of less than or equal to 0.8 . (4) A forward TTC value of less than or equal to 1.8 s, coupled with an acceleration or deceleration greater than or equal to 0.35 g , a forward range of less than or equal to 150 ft, a yaw rate of less than or equal to 4/s , and an azimuth of less than or equal to 0.8. Swerve (S) (5) Swerve value of greater than or equal to 3. Speed greater than or equal to 15 mph. Critical incident (CI) button (6) Activated by the driver pressing a button, located by the driver's visor, when an incident occurred that he or she deemed critical. Analyst identified (AI) (7) Event identified by a data reductionist viewing video footage; no other trigger listed above identified the event (e.g., LA and TTC).

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36 greater are always coded as crash-relevant conflicts or near crashes. Crash-relevant conflicts (no evasive maneuver) are classified similar to near crashes (no evasive maneuver). Here a TTC of 1 s is used to identify near crashes. Cases with a TTC between 1 and 2 s relied on subjective judgment (5). Relevant information was coded in a data dictionary that includes more than 50 variables (Appendix C). Figure 4.7 provides the flowchart showing the process of data reduction. After data reduction, 915 safety-relevant events were identi- fied; of these, there were 14 crashes, 14 tire strikes, 98 near crashes, and 789 crash-relevant conflicts. A random sample of 1,072 baseline epochs was also selected to represent normal driving. Each baseline epoch is 60 s long. The data dictionary was also applied to those baseline epochs. The purpose of select- ing baseline events was to provide a comparison with the events in that the baseline events represent normal driving. Figure 4.8 Figure 4.8. Screenshot from Project 7. shows a screenshot of the video data in this study (5). In characterizing traffic density at the time the event hap- pened, a detailed description was provided to the data reduc- venience provided to the motorist, passenger, or pedestrian tionists to assist in assigning the LOS. According to the report is excellent; from Project 7 (5), six levels of traffic density are defined plus 2. LOS B/Flow with some restrictions: In the range of stable a status of unknown or unable to determine: traffic flow, but the presence of other users in the traffic stream begins to be noticeable. Freedom to select desired 1. LOS A/Free flow: Individual users are virtually unaffected by speeds is relatively unaffected, but there is a slight decline the presence of others in the traffic. Freedom to select in the freedom to maneuver within the traffic stream from desired speeds and to maneuver within the traffic stream LOS A because the presence of others in the traffic stream is extremely high. The general level of comfort and con- begins to affect individual behavior; 3. LOS C/Stable flow: Maneuverability and speed are more restricted. In the range of stable traffic flow but marks the Start Data Scanning beginning of the range of flow in which the operation of individual users becomes significantly affected by the inter- actions with others in the traffic stream. The selection of speed is now affected by the presence of others, and maneu- Events Database vering within the traffic stream requires substantial vigi- lance on the part of the user. The general level of comfort No and convenience declines noticeably at this level; Valid Events? 4. LOS D/Unstable flow: Temporary restrictions substantially slow driver. Represents high-density and unstable traffic Yes flow. Speed and freedom to maneuver are severely restricted, No and the driver or pedestrian experiences a generally poor Conflicts? level of comfort and convenience. Small increases in traffic flow will generally cause operational problems at this level; Yes 5. LOS E: Vehicles are unable to pass and there are temporary stoppages. Represents operating conditions at or near the Classified and reduced in capacity level. All speeds are reduced to a low but relatively data dictionary: Crash uniform value. Freedom to maneuver within the traffic Crash: Tire strike End stream is extremely difficult, and it is generally accom- Near crash plished by forcing a vehicle or pedestrian to yield to accom- Crash-relevant conflict modate such maneuvers. Comfort and convenience levels are extremely poor, and driver or pedestrian frustration is generally high. Operations at this level are usually unstable Figure 4.7. Flowchart for data reduction because small increases in flow or minor perturbations in Project 7. within the traffic stream will cause breakdowns;

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37 Table 4.7. Level of Service for Crashes and Near Crashes No. of % of No. of % of No. of % of Crashes: Crashes: Near Near Traffic Density Crashes Crashes Tire Strikes Tire Strikes Crashes Crashes LOS A 13 92.9% 9 64.3% 61 62.2% LOS B 1 7.1% 1 7.1% 21 21.4% LOS C 0 0.0% 4 28.6% 11 11.2% LOS D 0 0.0% 0 0.0% 1 1.0% LOS E 0 0.0% 0 0.0% 2 2.0% LOS F 0 0.0% 0 0.0% 2 2.0% Unknown 0 0.0% 0 0.0% 0 0.0% Total 14 100.0% 14 100.0% 98 100.0% 6. LOS F: Forced traffic flow condition with low speeds and Weather conditions were coded as "No adverse conditions," traffic volumes that are below capacity; queues form in par- "Rain," "Sleet," "Snow," "Fog," "Rain and fog," "Sleet and fog," ticular locations. This condition exists whenever the amount "Other," and "Unknown." Table 4.8 shows the numbers and of traffic approaching a point exceeds the amount that can percentages of crashes, crashes: tire strikes, and near crashes traverse the point. Queues form behind such locations. associated with each weather condition. Operations within the queue are characterized by stop-and- go waves, and they are extremely unstable. Vehicles may Project 8: Naturalistic Truck Driving Study progress at reasonable speeds for several hundred feet or more and then be required to stop in a cyclic manner. LOS F Data reduction for the NTDS involved two main steps. Step 1 is used to describe the operating conditions within the queue, was to identify events of interest. The DART was used to as well as the point of the breakdown. In many cases, oper- find events of interest by scanning the data set for notable ating conditions of vehicles or pedestrians discharged from actions, including hard braking, quick steering maneuvers, the queue may be quite good. It is the point at which arrival short TTCs, and lane deviations. Table 4.9 displays the vari- flow exceeds discharge flow that causes the queue to form, ous trigger threshold values (6). VTTI researchers developed and LOS F is an appropriate designation for such points. the values based on data reduction experience obtained from the 100-Car Study. A 75-s epoch was created for each trigger Table 4.7 shows the numbers and percentages of crashes, comprising 1 min before the trigger and 15 s after the trigger. crashes: tire strikes, and near crashes associated with each LOS. The result of the automatic scan was an event data set that Table 4.8. Weather Condition When Events Happened No. of % of No. of % of No. of % of Crashes: Crashes: Near Near Weather Crashes Crashes Tire Strikes Tire Strikes Crashes Crashes No adverse conditions 11 78.6% 14 100.0% 91 92.9% Rain 2 14.3% 0 0.0% 7 7.1% Sleet 0 0.0% 0 0.0% 0 0.0% Snow 0 0.0% 0 0.0% 0 0.0% Fog 1 7.1% 0 0.0% 0 0.0% Rain and fog 0 0.0% 0 0.0% 0 0.0% Sleet and fog 0 0.0% 0 0.0% 0 0.0% Other 0 0.0% 0 0.0% 0 0.0%

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38 Table 4.9. Trigger Values Used to Identify Critical Incidents in Project 8 Trigger Type Definition Description Longitudinal acceleration (LA) Hard braking or sudden acceleration Acceleration or deceleration greater than or equal to 0.20 g . Speed greater than or equal to 1 mph (1.6 km/h). Time to collision (TTC) Amount of time (in seconds) it would take A forward TTC value of less than or equal to 2 s, coupled for two vehicles to collide if one vehicle with a range of less than or equal to 250 ft, a target did not perform an evasive maneuver. speed of greater than or equal to 5 mph (8 km/h), a yaw rate of less than or equal to 6/s , and an azimuth of less than or equal to 0.12 . Swerve (S) Sudden jerk of the steering wheel to return Swerve value of greater than or equal to 2 rad/s2. Speed the truck to its original position in the greater than or equal to 5 mph (8.05 km/h). lane. Lane deviations (LD) Any time the truck aborts the lane line and Lane tracker status equals abort. Distance from center of returns to the same lane without making lane to outside of lane line less than 44 in. a lane change. Critical incident (CI) button Self-report of an incident by the driver. Activated by the driver pressing a button by the driver's visor when an incident occurred that he or she deemed critical. Analyst identified (AI) Event identified by the analyst but not by a Event that was identified by a data analyst viewing video trigger. footage; no other trigger listed above identified the event (e.g., LA and TTC). included both valid and invalid events waiting to be further ated video data and answering questions in a pull-down identified in step 2. menu in the DART. Invalid events were eliminated when sen- Step 2 involved a manual inspection of these potential sor readings were spurious because of a transient spike or events of interest by data reductionists to filter out invalid some other anomaly (i.e., false positive). Appendices C and D events. Figure 4.9 shows a screenshot of the video data. provide the data dictionary for event and environmental cri- Valid events were further classified into one of six safety- teria, respectively. critical events: crash, crash: tire strike, near crash, crash- Most of the events happened during smooth traffic condi- relevant conflict, unintentional lane deviation, and illegal tions and nonadverse weather conditions. Tables 4.11 and 4.12 maneuver. Table 4.10 summarizes the definitions of these show the details of the LOS and weather categories, respec- event types (6). tively. The traffic and weather conditions at the instant the The details of each valid event were coded by reductionists crashes and near crashes occurred should not be used alone to using an established coding directory by watching the associ- decide if these factors have an impact on the likelihood of the safety-related events. The percentage of heavy traffic and adverse weather conditions occurring in baseline epochs should be compared with the event epochs. For example, heavy traffic occurs in 10% of the baseline epochs but is present in 20% of the event epochs. Although 20% is not a high percent- age, it is a factor worthy to note. Project 11: Naturalistic Teen Driving Study Data reduction procedures in this study included three tasks: initial data reduction, straight road segment data reduction, and event data reduction. The initial data reduc- tion involved recording general information about the driver and passengers of each trip. A trip was defined as a con- tinuous data collection from the start of the engine of a partic- ipant's vehicle to its turn-off. The recorded variables included Figure 4.9. Screenshot of video data in Project 8. participant ID, number of passengers, age of passengers,

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39 Table 4.10. Event Types in Project 8 Event Type Description Crash Any contact with an object, either moving or fixed, at any speed. Crash: tire strike Any contact with an object, either moving or fixed, at any speed in which kinetic energy is measurably trans- ferred or dissipated when the contact occurs only on the truck's tire. No damage occurs during these events (e.g., a truck is making a right turn at an intersection and runs over the sidewalk or curb with a tire). Near crash Any circumstance that requires a rapid, evasive maneuver (e.g., hard braking, steering) by the subject vehicle or any other vehicle, pedestrian, cyclist, or animal to avoid a crash. Crash-relevant conflict Any circumstance that requires a crash-avoidance response on the part of the subject vehicle, any other vehi- cle, pedestrian, cyclist, or animal that was less severe than a rapid evasive maneuver (as defined above) but more severe than a normal maneuver. A crash-avoidance response can include braking, steering, accelerating, or any combination of control inputs. Unintentional lane deviation Any circumstance in which the subject vehicle crosses over a solid lane line (e.g., onto the shoulder) where there is not a hazard (guardrail, ditch, or vehicle) present. Illegal maneuver Any circumstance in which either the subject vehicle or the other vehicle performs an illegal maneuver, such as passing another vehicle across the double yellow line or on a shoulder. In many cases, neither driver performs an evasive action. Table 4.11. LOS for Crashes and Near Crashes in Project 8 No. of % of No. of % of No. of % of Crashes: Crashes: Near Near Traffic LOS Crashes Crashes Tire Strikes Tire Strikes Crashes Crashes LOS A 3 60.0% 4 50% 23 37.7% LOS B 2 40.0% 3 37.5% 22 36.1% LOS C 0 0.0% 0 0.0% 13 21.3% LOS D 0 0.0% 0 0.0% 1 1.6% LOS E 0 0.0% 1 12.5% 2 3.3% LOS F 0 0.0% 0 0.0% 0 0.0% Unknown 0 0.0% 0 0.0% 0 0.0% Total 5 100.0% 8 100.0% 61 100.0% Table 4.12. Weather Condition for Crashes and Near Crashes in Project 8 No. of % of No. of % of No. of % of Crashes: Crashes: Near Near Weather Crashes Crashes Tire Strikes Tire Strikes Crashes Crashes No adverse conditions 5 100.0% 8 100.0% 56 91.8% Rain 0 0.0% 0 0.0% 5 8.2% Sleet 0 0.0% 0 0.0% 0 0.0% Snow 0 0.0% 0 0.0% 0 0.0% Fog 0 0.0% 0 0.0% 0 0.0% Rain and fog 0 0.0% 0 0.0% 0 0.0% Sleet and fog 0 0.0% 0 0.0% 0 0.0% Other 0 0.0% 0 0.0% 0 0.0% Total 5 100.0% 8 100.0% 61 100.0%

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40 Table 4.13. Trigger Types in Project 11 Trigger Type Description Longitudinal deceleration (LD) Less than or equal to -0.65 g Lateral acceleration Greater than or equal to 0.75 g or less than or equal to -0.75 g Forward time to collision (FTTC) FTTC less than or equal to 4 s with a deceleration less than or equal to -0.6 g. FTTC less than or equal to 4 s paired with a deceleration less than or equal to -0.5 g and forward range of less than or equal to 100 ft. Yaw rate Vehicle swerves 4 degrees/s to 4 degrees/s within a window of 3.0 s Longitudinal acceleration (LA) Greater than or equal to 0.5 g, returning to 0.1 g within 0.2 s Critical incident (CI) button Boolean response Speeding trigger In excess of 70 mph but not traveling on an interstate and time of day. For the straight road segment reduction, Figure 4.10 illustrates some sample screenshots for the data reductionists recorded a set of variables for 22 previ- NTNDS. The left screenshot is a snapshot from the continu- ously selected straight road segments that were chosen based ous video data captured by four continuous camera views on the frequency of travel. The recorded variables included monitoring the driver's face and the driver side of the vehicle, driver behavior and performance in an attempt to analyze the forward view, the rear view, and an over-the-shoulder driver engagement in secondary tasks and the driver's eye- view for the driver's hands and surrounding areas. In the right scanning patterns. For the event reduction, potential events screenshot, the bottom photos show sample periodic still shots were initially identified by the kinematic trigger values, and by two other cameras in the interior vehicle cabin, as well as trained reductionists reviewed the video data to confirm the lap area of the rear passenger seat. their validity. The details of the trigger values are described Data reduction for this study is ongoing, and not much can in Table 4.13. be presented in this report. Because this study is the most recent naturalistic study accomplished by VTTI, all the equipment Valid events were further classified by data reductionists involved is the most updated and highly accurate. The data into three categories--crash, near crash, and judgment error-- reduction protocol is refined so that the threshold values selected based on their severity. A crash is defined as "any contact are more reasonable, and data reductionists have acquired with an object, either moving or fixed, at any speed in which more experience from previous studies. The resulting data set kinetic energy is measurably transferred or dissipated. Includes will be beneficial in the next stage of research for this study. other vehicles, roadside barriers, objects on or off the road- way, pedestrians, cyclists, or animals." A near crash is defined as "any circumstance that requires a rapid evasive maneu- Table 4.14. List of Variables in Reduced Data ver by the subject vehicle or any other vehicle, pedestrian, in Project 11 cyclist, or animal to avoid a crash. A rapid evasive maneuver is defined as steering, braking, accelerating, or any combina- Classification Examples tion of control inputs that approaches the limits of the vehi- Vehicle variables Vehicle ID, vehicle type, owned or shared, cle capabilities." and VMT Judgment error is defined as "any circumstance where the Event variables Nature of event or crash type, pre-event maneu- teen driver purposefully or inadvertently creates a safety- ver, precipitating factors, corrective action or relevant situation due to either general inexperience or per- evasive maneuver, contributing factors, types formance error. Examples include those events where the of inattention, driver impairment teen drivers engage in `horseplay' or overreact to surround- Environmental Weather, ambient lighting, road type, traffic ing traffic" (7). variables density, relation to junction, surface condi- tion, traffic flow For crash and near crash events, a series of variables were coded during the data reduction process. The variables are Driver's state Hands on wheel, seat belt usage, fault assign- ment, eyeglance broadly classified into several homogeneous groups as vehicle, event, environmental, driver's state, and second vehicle vari- Driver of vehicle 2 Vehicle 2 body style, maneuver, corrective action attempted ables, as summarized in Table 4.14.