Characterization of Lane Departure Crashes Using Event Data Recorders Extracted from Real-World Collisions

Impact of event encoding and dissimilarity measures on traffic crash characterization based on sequence of events

Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016 to 2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration.

Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

This paper introduces a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016-2018 National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS) database. Vehicles involved in the crashes are specifically renumbered based on their initial positions and trajectories. Crash sequences are encoded to include detailed pre-crash events and concise collision events. Based on sequence patterns, the crashes are characterized as 55 types. A Bayesian network model is developed to depict the interrelationships among crash sequence types, crash outcomes, human factors, and environmental conditions. Scenarios are specified by querying the Bayesian network's conditional probability table. Distributions of operational design domain (ODD) attributes (e.g., driver behavior, weather, lighting condition, intersection geometry, traffic control device) are specified based on conditions of sequence types. Also, distribution of sequence types is specified on specific crash outcomes or combinations of ODD attributes.

Expansion of NASS/CDS for characterizing run-off-road crashes

OBJECTIVE

Run-off-road (ROR) crashes account for one-third of all annual crash fatalities in the US. The National Automotive Sampling System Crashworthiness Data System (NASS/CDS) is a dataset which may be used to understand the nature of ROR crashes. Despite the wealth of coded data available in NASS/CDS, this dataset lacks coded information about the roadside environment and the off-road trajectory of the vehicle. This information would be useful for determining lane departure warning (LDW) benefits, residual safety problems, performance of current safety hardware, lane marking inventory, LDW test procedure development, radius of curvature characterization, and effectiveness of ESC. The purpose of this paper is to demonstrate a methodology for expanding the data available in NASS/CDS to form and validate a specialized road departure database.

METHODS

Observed, measured, and reconstructed data elements were extracted from NASS/CDS and compiled into the National Cooperative Highway Research Program (NCHRP) 17-43 database. Observed variables were primarily coded from the scene photographs and included information such as the lane markings, and geometry of the roadside cross-section. Additional variables were measured from the scaled scene diagrams including the path of the vehicle, road dimensions, and roadside object positions. The vehicle impact speed and departure speed were reconstructed using the WinSMASH delta-v, roadside object characteristics, and vehicle path. Two studies were conducted to demonstrate the usefulness of the NCHRP 17-43 database in evaluating both vehicle-based and infrastructure-based ROR countermeasures.

RESULTS

The resulting NCHRP 17-43 database includes 1,581 NASS/CDS cases representing 510,154 ROR crashes. Analysis of the database found that drivers which crashed following an overcorrection were younger than drivers which did not overcorrect. This may indicate that inexperienced drivers are more likely to overcorrect when departing the roadway. The 85^th percentile impact severity of ROR crashes, which occur on roads with a speed limit greater than 65 mph, is higher than the practical worst-case test conditions for roadside barriers.

CONCLUSIONS

The NCHRP 17-43 database contains information extracted from NASS/CDS cases to better understand the nature of ROR crashes, driver behavior in these crashes, and the potential benefits of both vehicle-based and infrastructure-based ROR countermeasures.

A video based run-off-road training program with practice and evaluation in a simulator

A run-off-road (ROR) event occurs when one or more of a vehicle's wheels leaves the roadway and begins to travel on the surface or shoulder adjacent to the road. Despite various countermeasures, ROR crashes continue to yield a large number of fatalities and injuries. Infrastructure-based solutions do not directly address the critical factor of driver performance preceding and during an ROR event. In this study, a total of 75 individuals participated in a pre-post experiment to examine the effect of a training video on improving driver performance during a set of simulated ROR scenarios (e.g., on a high speed highway, a horizontal curve, and a residential rural road). In each scenario, the vehicle was unexpectedly forced into an ROR scenario for which the drivers were instructed to recover as safely as possible. The treatment group then watched a custom ROR training video while the control group viewed a placebo video. The participants then drove the same simulated ROR scenarios. The results suggest that the training video had a significant positive effect on drivers' steering response on all three roadway conditions as well as improvements in vehicle stability, subjectively rated demand on the driver, and self-evaluated performance in the highway scenario. Under the highway conditions, the treatment group reduced the frequency of spinouts from 70% in the pre-training events to 16% in the post-training events (χ(2)(1)=23.32, p<0.001) with no significant improvement found for the control group. In the horizontal curve, spinouts were reduced for the treatment group from 50% in the pre-training events to 30% in the post-training events (χ(2)(1)=8.45, p=0.004) with the control group also not showing any significant improvement. The results of this study suggest that even a short video about recovering from ROR events can significantly influence a driver's ability to recover. It is possible that additional training may have further benefits in recovering from ROR events.