Use of Event Data Recorder (EDR) Technology for Highway Crash Data Analysis (2005) / Chapter Skim
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From page 57... ...
43 3. EDR Data Needs for Roadside Safety Analyses: Identification and Prioritization 3.1 Objective The success of roadside and vehicle safety research is critically dependent upon the validity and consistency of collected crash data.
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44 Literature Review of Roadside Safety Data Needs. The research team conducted an extensive review of the roadside safety technical literature to identify recommended improvements to data elements presently collected, and to identify data elements not presently captured that could be of significant value to the roadside safety community.
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45 • Are advanced occupant restraint systems, e.g., dual stage inflator systems, performing as designed? • How accurate are the delta-V estimates in U.S.
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46 was performed by Kullgren et al (1995, 1998, 2000) using the Crash Pulse Recorder, a retrofit EDR developed specifically for these research studies.
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47 Table 3-1. Data Needs for Roadside Safety Analysis as expressed in the Research Literature Data Element V i n e r , J a n u a r y 1 9 9 5 M a k , 1 9 9 5 H u n t e r a n d C o u n c i l , 1 9 9 6 M a k a n d S i c k i n g , 1 9 9 4 R o s s e t a l .
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48 Data Element V i n e r , J a n u a r y 1 9 9 5 M a k , 1 9 9 5 H u n t e r a n d C o u n c i l , 1 9 9 6 M a k a n d S i c k i n g , 1 9 9 4 R o s s e t a l .
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49 Data Element V i n e r , J a n u a r y 1 9 9 5 M a k , 1 9 9 5 H u n t e r a n d C o u n c i l , 1 9 9 6 M a k a n d S i c k i n g , 1 9 9 4 R o s s e t a l .
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50 Data Element V i n e r , J a n u a r y 1 9 9 5 M a k , 1 9 9 5 H u n t e r a n d C o u n c i l , 1 9 9 6 M a k a n d S i c k i n g , 1 9 9 4 R o s s e t a l .
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51 Table 3-2. Research Data Needs vs.
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52 3.4 Examination of Existing Accident Databases One of the most important near-term uses of EDR data will be to improve the collection of data for existing accident databases. This section describes the methodology, analysis, and results of a study to determine the potential of using EDR data to augment data collection for roadside and vehicle crashes.
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53 All examples in the methodology refer to elements in the FARS database; this methodology has been applied to the remaining databases and collection protocols. 3.4.1 Classification Methodology Due to the number of elements and range of data present in the existing databases, the research team recognized the need for a methodical approach to the classification of the data elements within each database.
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54 several vehicles and a number of persons, accident records are generally organized as relational databases of linked tables. FARS for example has three tables – (1)
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55 • Additional Classification Conventions. The assumption that electronic instrumentation is available is based on the most probable method of instrumentation.
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56 database, it must involve a motor vehicle traveling on a primarily public roadway and death of an individual involved within 30 days of the incident. Each incident is characterized by the collection of approximately 175 data elements split among an accident table, a vehicle table, and a person table.
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57 Table 3-4. FARS-EDR Compatibility Variable Name Variable Description Current Technology Future Technology Notes TRAV_SP Estimation of vehicle travel speed X DR_CF1 Driver related factors (often indicates the cause of the crash)
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58 Variable Name Variable Description Current Technology Future Technology Notes MOD_YEAR Indicates the model year of the vehicle X 4 MONTH Month when the crash occurred X EMER_USE Indicates whether the vehicle was in emergency use at the time of the crash X 10 DR_WGT Indicates the weight of the driver in pounds X 2 SCH_BUS Indicates whether the accident involved a school bus functioning as such X 5 PER_TYP Indicates situation of occupant (driver, passenger of vehicle in motion, passenger of vehicle not in motion, etc.) X 2 OCCUPANTS Actual number of occupants in the vehicle at the time of the crash X 2 WEATHER Indicates atmospheric conditions at the time of crash X 6 CITY City code based on GSA codes X 7 LATITUDE Global position of the crash location (latitude)
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59 Direct: EDR data can be obtained directly from the EDR and transferred to the database without any intermediate inference (note that mathematical operations to convert EDR raw data are ignored)
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60 Table 3-5. NASS/CDS Extracted Data Elements Variable Name Variable Description Current Technology Future Technology Notes DAYWEEK Day of week of the accident X EVENTS Number of recorded events in accident X MANCOLL Manner of collision X MONTH Month of accident X TIME Time of accident X YEAR Year of accident X ANGTHIS Heading angle for this vehicle X ANTILOCK Antilock brakes X BAGDEPFV Air bag deployment, first seat frontal X BAGDEPOV Air bag deployment, other X BODYTYPE Vehicle body type X 1 CARBUR Carburetion X 1 CURBWGT Vehicle curb weight X 1 DRIVE Front/rear wheel drive X 1 DRPRES Driver presence in vehicle X 2 DVEST Estimated highest delta v X DVLAT Lateral component of delta v X DVLONG Longitudinal component of delta v X DVTOTAL Total delta v X FOURWHDR Four wheel drive X 1 FRTWHLDR Front wheel drive X 1 IMPACTSP Impact speed X LGTCOND Light conditions X 3 MAKE Vehicle make X 1 MANEUVER Attempted avoidance maneuver X 6 MODEL Vehicle model X 1 MODELYR Vehicle model year X 1 PREISTAB Pre-impact stability X 5 RESTYPE Restraint type X 1 ROLINDIR Direction of initial roll X ROLLOVER Rollover X SERTR VIN series truck X 1 SURCOND Roadway surface condition X 6 VEHTYPE Type of vehicle X 1 VEHUSE Vehicle special use (This trip)
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61 Variable Name Variable Description Current Technology Future Technology Notes BAGDEPLY Air bag system deployed X BAGDEPOT Other air bag system deployment X BAGFAIL Air bag system failure X DVBAG Longitudinal component of delta v for airbag deployment X MANAVAIL Manual belt system availability X MANUSE Manual belt system use X ROLE Occupant's role X 2 SEATPOS Occupant's seat position X 2 WEIGHT Occupant's weight X 2 DOF1 Direction of force (highest) X DOF2 Direction of force (2nd highest)
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62 Table 3-6. NASS/GES Extracted Data Elements Variable Name Variable Description Current Technology Future Technology Notes MONTH Month in which the crash occurred X YEAR Year in which the crash occurred (four digits)
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63 4- May be inferred from usage of windshield wipers 5- May be inferred from precrash information from ABS systems or steering or braking information 6- Assumes stability can be inferred from yaw, pitch, roll vs. time The reader should refer to the appendices for a complete listing and classification of the variables contained in the NASS/GES database.
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64 Table 3-8. HSIS Extracted Data Elements Variable Name Variable Description Current Technology Future Technology Notes ACC_DATE Accident date X ACCYR Accident year X DAYMTH Day of month X HOUR Hour of occurrence X MONTH Month of accident X WEEKDAY Day of week X LIGHT Light condition X 3 RDSURF Surface road condition X 5 WEATHER Weather condition X 4 CONTRIB1 Accid contrib factor(s)
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65 Table 3-9. LBSS Extracted Data Elements Variable Name Variable Description Current Technology Future Technology Notes LGTCOND Light conditions at the time of the accident X 3 MANCOLL Manner of collision based on first harmful event X 5 TIME Time of the accident X WEATHER Atmospheric conditions at the time of the accident X 4 B62 Impact angle X 5 B63 Vehicle yawing angle at impact X 5 B64 Impact speed X B65 Separation angle X 5,6 B67 Post-impact trajectory X 6 B69 Rollover X B71 Confidence of impact angle X B72 Confidence of yawing angle at impact X B73 Confidence of separation angle X 6 B74 Confidence of final rest distance X 6 AVOIDMAN Attempted avoidance maneuver X 7 OCUPANTS Number of occupants (this vehicle)
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66 7- May be inferred from precrash information from ABS systems or steering or braking information The reader should refer to the appendices for a complete listing and classification of the variables contained in the LBSS database. The variables have been split according to the file system used in the LBSS database.
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67 Variable Name Variable Description Current Technology Future Technology Notes VD2 Vehicle Model X 1 VD3 Vehicle Body Type X 1 Notes: 1- Can be derived from VIN and associated Vehicle Parameters Database 2- Assumes a weight sensor in all vehicle seating positions. 3- May be inferred from usage of lights 4- May be inferred from usage of windshield wipers 5- May be inferred from precrash information from ABS systems or steering or braking information 6- Can be estimated for EDRs that record acceleration on multiple axes The reader should refer to the appendices for a complete listing and classification of the variables contained in the MMUCC.
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68 Variable Name Variable Description Current Technology Future Technology Notes ENGDSP Test vehicle engine displacement (liters) X 1 WHLBAS Measured or published value for the vehicle or impactor's wheelbase X 1 VEHLEN Measured or published value for the length of the vehicle or impactor X 1 VEHWID Maximum width of the vehicle or impactor X 1 VEHSPD Resultant speed of the vehicle immediately before impact X PDOF Principal direction of force - angle between the vehicle's longitudinal axis and the impulse vector (clockwise is positive)
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69 which describe the physical characteristics and dynamic performance of both the vehicle and the tested device [Ross et al, 1993]
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70 3.4.10 NCHRP 22-15 Recommended NASS/CDS Data Elements The main objectives of NCHRP Project 22-15, entitled "Improving the Compatibility of Vehicles and Roadside Safety Hardware", were to investigate the compatibility between vehicles and roadside safety hardware and to assess opportunities and barriers to improving compatibility. While fulfilling these objectives, Eskandarian et al (2002)
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71 Table 3-14 presents the results of the examination of the TIFA database and subsequent extraction of potential EDR data elements. Table 3-14.
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72 Variable Name Variable Description Current Technology Future Technology Notes VehicleMake Vehicle make X 1 VehicleModel Vehicle model X 1 Weather Weather condition X 4 WeightClass Vehicle weight (using VIN series) , by weight class X 1 Year Year in which accident occurred X Notes: 1- Can be derived from VIN and associated Vehicle Parameters Database 2- Assumes a weight sensor in all vehicle seating positions.
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73 Variable Name Variable Description Current Technology Future Technology Notes Rdsurf The condition of the road surface at the time and location of the crash. X 4 State State abbreviation in which crash occurred X StateName State name in which crash occurred X Truckbus Indication of whether the vehicle involved in the crash was a truck (t)
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74 3.5 Summary of Data Elements Which Could Be Collected by EDRs A universal catalog was assembled containing the potential EDR data elements extracted from the literature review and the target databases. EDR elements not currently collected for the target databases but representing a possible contribution to roadside safety have been identified and integrated into this listing.
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75 Variable Description EDR Element(s) Notes Acceleration (X,Y,Z directions)
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76 Notes 1. Vehicle Identification Number + Vehicle Parameter Database 2.
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77 3.6 Prioritization of EDR Data Elements for Roadside Safety Analysis This section presents a prioritization of EDR data elements by their importance to roadside safety analysis. Earlier sections of this report have presented (1)
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78 assigned to the most important of the 10 data elements, a score of 1 was to be assigned to the least important of the 10 elements, and a blank score assigned to any unranked elements. The teams were also encouraged to suggest additional elements which were not on the list of EDR data elements.
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79 • Data elements measuring crash performance of the vehicle-roadside system were also of high priority (4 of the top 10) and included lateral delta-V, longitudinal delta-V, lateral crash pulse, and longitudinal crash pulse.
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80 Table 3-18. OEM Event Data Recorder Data Elements Category Data Element/Description Priority PreCrash Acceleration - lateral Acceleration - longitudinal Brake Position (% where 0 is no brake and 100 is full brake)
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81 Table 3-19. Results of EDR Data Elements Prioritization Exercise Scenario 1 – Revised NCHRP 350 Scenario 2 – Super SUVs Scenario 3 – Rural Roads w/ Ditches Category Data Element/Description Team1 Team2 Team3 Team4 Team5 Team6 Team7 Team8 Scena rio 1 Score Scena rio 1 Score Scena rio 1 Score Total Score PreCrash Acceleration - lateral 2 3 9 5 5 9 5 19 Acceleration - longitudinal 1 4 8 4 5 8 4 17 Brake Position 7 4 3 8 7 4 11 22 Clutch status (position or %)
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82 Results of EDR Data Elements Prioritization Exercise (continued) Scenario 1 – Revised NCHRP 350 Scenario 2 – Super SUVs Scenario 3 – Rural Roads w/ Ditches Category Data Element/Description Team1 Team2 Team3 Team4 Team5 Team6 Team7 Team8 Scena rio 1 Score Scena rio 1 Score Scena rio 1 Score Total Score Restraint Air bag deployment attempt made (yes/no)
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83 Table 3-20. Summary of Results of the EDR Data Elements Prioritization Exercise Overall Priority Data Element Data Element Category Scenario 1 Rank Scenario 2 Rank Scenario 3 Rank Total Score 1 Vehicle speed Precrash 1 1 2 68 2 Yaw Rate Precrash 2 6 3 49 3 Location of Crash Environment 6 2 1 48 4 Delta-V longitudinal Crash 4 5 4 43 5 Delta-V lateral Crash 5 4 5 39 6 Roll rate Precrash 3 14 9 31 7 Brake Position Precrash 8 13 6 22 8 Crash-pulse lateral Crash 9 10 7 22 9 Crash-pulse long.
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84 Table 3-21. EDR Data Element Priority for Roadside Safety Analysis Overall Priority Data Element Data Element Category Current EDR Technology Future EDR Technology 1 Vehicle speed Precrash X 2 Yaw Rate Precrash X 3 Location of Crash Environment X 4 Delta-V longitudinal Crash X 5 Delta-V lateral Crash X 6 Roll rate Precrash X 7 Brake Position Precrash X 8 Crash-pulse lateral Crash X 9 Crash-pulse long.
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85 3.7 Recommended EDR Data Elements As the preceding analysis has shown, the use of EDR technology is a promising method to augment data collection for existing roadside and vehicle accident databases. In addition, EDR technology can support future roadside safety research needs by providing a new source of crash data previously not feasible to collect.
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86 Data Element / Description Current EDR Technology NHTSA NPRM Element Priority Roadside Element Side Airbag, Passenger, Time to Deployment x x Side Curtain/Tube Driver, Time to Deployment x x Side Curtain/Tube Passenger Time to Deployment x x Time between Events x x Vehicle speed x x x Antilock braking (engaged / non-engaged) x Brake Status, Engine (on / off)
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87 • The Ignition Cycle Count, a data element currently being stored in some EDRs, is recommended as an interim measure of when the crash occurred. Once EDRs begin to store crash date and time, Ignition Cycle Count should no longer be needed.
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88 3.8 Recommendations for EDR Enhancement Current EDR capabilities will need to be enhanced to better support roadside crash analysis. Following is a list of recommendations: • Increase the EDR recording duration.
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89 deployment level event, which occurs after bag deployment, can record over a non-deployment event. Even these newer GM devices however can only capture two events.
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90 3.9 Conclusions The objective of this chapter was to catalog and prioritize EDR data needs which support vehicle and roadside safety research and design. The specific objectives were to determine the potential of EDR technology (1)
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91 Our recommendations are: • Based on a comparison of EDR capabilities and highway crash data analysis needs, a catalog of 66 recommended data elements has been developed. Nearly half of these data elements, are already being stored in production vehicle EDRs.
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92 3.10 References Bligh, Roger P "Performance of Current Safety Hardware for NCHRP 350 Vehicles".
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93 FMCSA, Motor Carrier Management Information System (MCMIS) Data Dissemination Catalog & Documentation, Federal Motor Carrier Safety Administration, http://mcmiscatalog.fmcsa.dot.gov/beta/Catalogs&Documentation/default.asp, (viewed December 2004)
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94 Mak, K K., "Problems Associated with Police-Level Accident Data in Evaluation of Roadside Appurtenance Performance," Transportation Research Circular No.
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95 NHTSA, National Automotive Sampling System (NASS) General Estimates System (GES)
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96 Ross, H E., Jr., Sicking, D
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