Effectiveness of Intersection Advanced Driver Assistance Systems in Preventing Crashes and Injuries in Left Turn Across Path / Opposite Direction Crashes in the United States
| dc.contributor.author | Bareiss, Max | en |
| dc.contributor.committeechair | Gabler, Hampton Clay | en |
| dc.contributor.committeemember | Hardy, Warren N. | en |
| dc.contributor.committeemember | Southward, Steve C. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2020-01-24T14:46:29Z | en |
| dc.date.available | 2020-01-24T14:46:29Z | en |
| dc.date.issued | 2019 | en |
| dc.description.abstract | Intersection crashes represent one-fifth of all police reported traffic crashes and one-sixth of all fatal crashes in the United States each year. Active safety systems have the potential to reduce crashes and injuries across all crash modes by partially or fully controlling the vehicle in the event that a crash is imminent. The objective of this thesis was to evaluate crash and injury reduction in a future United States fleet equipped with intersection advanced driver assistance systems (I-ADAS). In order to evaluate this, injury risk modeling was performed. The dataset used to evaluate injury risk was the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). An injured occupant was defined as vehicle occupant who experienced an injury of maximum Abbreviated Injury Scale (AIS) of 2 or greater, or who were fatally injured. This was referred to as MAIS2+F injury. Cases were selected in which front-row occupants of late-model vehicles were exposed to a frontal, near-, or far-side crash. Logistic regression was used to develop an injury model with occupant, vehicle, and crash parameters as predictor variables. For the frontal and near-side impact models, New Car Assessment Program (NCAP) test results were used as a predictor variable. This work quantitatively described the injury risk for a wide variety of crash modes, informing effectiveness estimates. This work reconstructed 501 vehicle-to-vehicle left turn across path / opposite direction (LTAP/OD) crashes in the United States which had been originally investigated in NMVCCS. The performance of thirty different I-ADAS system variations was evaluated for each crash. These variations were the combinations of five Time to Collision (TTC) activation thresholds, three latency times, and two different intervention types (automated braking and driver warning). In addition, two sightline assumptions were modeled for each crash: one where the turning vehicle was visible long before the intersection, and one where the turning vehicle was only visible after entering the intersection. For resimulated crashes which were not avoided by I-ADAS, a new crash delta-v was computed for each vehicle. The probability of MAIS2+F injury to each front row occupant was computed. Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18%-84% of all LTAP/OD crashes. An I-ADAS system which applies emergency braking could prevent 44%-94% of front row occupants from receiving MAIS2+F injuries. I-ADAS crash and injured person reduction effectiveness was higher when both vehicles were equipped with I-ADAS. This study presented the simulated effectiveness of a hypothetical intersection active safety system on real crashes which occurred in the United States, showing strong potential for these systems to reduce crashes and injuries. However, this crash and injury reduction effectiveness made the idealized assumption of full installation in all vehicles of a future fleet. In order to evaluate I-ADAS effectiveness in the United States fleet the proportion of new vehicles with I-ADAS was modeled using Highway Loss Data Institute (HLDI) fleet penetration predictions. The number of potential LTAP/OD conflicts was modeled as increasing year over year due to a predicted increase in Vehicle Miles Traveled (VMT). Finally, the combined effect of these changes was used to predict the number of LTAP/OD crashes each year from 2019 to 2060. In 2060, we predicted 70,439 NMVCCS-type LTAP/OD crashes would occur. The predicted number of MAIS2+F injured front row occupants in 2060 was 3,836. This analysis shows that even in the long-term fleet penetration of Intersection Active Safety Systems, many injuries will continue to occur. This underscores the importance of maintaining passive safety performance in future vehicles. | en |
| dc.description.abstractgeneral | Future vehicles will have electronic systems that can avoid crashes in some cases where a human driver is unable, unaware, or reacts insufficiently to avoid the crash without assistance. The objective of this work was to determine, on a national scale, how many crashes and injuries could be avoided due to Intersection Advanced Driver Assistance Systems (I-ADAS), a hypothetical version of one of these up-and-coming systems. This work focused on crashes where one car is turning left at an intersection and the other car is driving through the intersection and not turning. The I-ADAS system has sensors which continuously search for other vehicles. When the I-ADAS system determines that a crash may happen, it applies the brakes or otherwise alerts the driver to apply the brakes. Rather than conduct actual crash tests, this was simulated on a computer for a large number of variations of the I-ADAS system. The basis for the simulations was real crashes that happened from 2005 to 2007 across the United States. The variations that were simulated changed the time at which the I-ADAS system triggered the brakes (or alert) and the simulated amount of computer time required for the I-ADAS system to make a choice. In some turning crashes, the car cannot see the other vehicle because of obstructions, such as a line of people waiting to turn left across the road. Because of this, simulations were conducted both with and without the visual obstruction. For comparison, we performed a simulation of the original crash as it happened in real life. Finally, since there are two cars in each crash, there are simulations when either car has the I-ADAS system or when both cars have the I-ADAS system. Each simulation either ends in a crash or not, and these are tallied up for each system variation. The number of crashes avoided compared to the number of simulations run is crash effectiveness. Crash effectiveness ranged from 1% to 84% based on the system variation. For each crash that occurred, there is another simulation of the time immediately after impact to determine how severe the impact was. This is used to determine how many injuries are avoided, because often the crashes which still happened were made less severe by the I-ADAS system. In order to determine how many injuries can be avoided by making the crash less severe, the first chapter focuses on injury modeling. This analysis was based on crashes from 2008 to 2015 which were severe enough that one of the vehicles was towed. This was then filtered down by only looking at crashes where the front or sides were damaged. Then, we compared the outcome (injury as reported by the hospital) to the circumstances (crash severity, age, gender, seat belt use, and others) to develop an estimate for how each of these crash circumstances affected the injury experienced by each driver and front row passenger. A second goal for this chapter was to evaluate whether federal government crash ratings, commonly referred to as “star ratings”, are related to whether the driver and passengers are injured or not. In frontal crashes (where a vehicle hits something going forwards), the star rating does not seem to be related to the injury outcome. In near-side crashes (the side next to the occupant is hit), a higher star rating is better. For frontal crashes, the government test is more extreme than all but a few crashes observed in real life, and this might be why the injury outcomes measured in this study are not related to frontal star rating. Finally, these crash and injury effectiveness values will only ever be achieved if every car has an I-ADAS system. The objective of the third chapter was to evaluate how the crash and injury effectiveness numbers change each year as new cars are purchased and older cars are scrapped. Early on, few cars will have I-ADAS and crashes and injuries will likely still occur at roughly the rate they would without the system. This means that crashes and injuries will continue to increase each year because the United States drives more miles each year. Eventually, as consumers buy new cars and replace older ones, left turn intersection crashes and injuries are predicted to be reduced. Long into the future (around 2050), the increase in crashes caused by miles driven each year outpaces the gains due to new cars with the I-ADAS system, since almost all of the old cars without I-ADAS have been removed from the fleet. In 2025, there will be 173,075 crashes and 15,949 injured persons that could be affected by the I-ADAS system. By 2060, many vehicles will have I-ADAS and there will be 70,439 crashes and 3,836 injuries remaining. Real cars will not have a system identical to the hypothetical I-ADAS system studied here, but systems like it have the potential to significantly reduce crashes and injuries. | en |
| dc.description.degree | M.S. | en |
| dc.format.medium | ETD | en |
| dc.identifier.uri | http://hdl.handle.net/10919/96570 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | ADAS | en |
| dc.subject | Active Safety | en |
| dc.subject | I-ADAS | en |
| dc.subject | injury risk modeling | en |
| dc.subject | NCAP | en |
| dc.subject | LTAP/OD | en |
| dc.title | Effectiveness of Intersection Advanced Driver Assistance Systems in Preventing Crashes and Injuries in Left Turn Across Path / Opposite Direction Crashes in the United States | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | M.S. | en |