Rini Sherony - Academia.edu (original) (raw)

Papers by Rini Sherony

arXiv (Cornell University), May 18, 2023

Automated vehicles (AVs) are of great potential in reducing crashes on the road. However, it is s... more Automated vehicles (AVs) are of great potential in reducing crashes on the road. However, it is still complicated to eliminate all the possible accidents, especially those with vulnerable road users (VRUs), who are among the greater risk than vehicle occupants in traffic accidents. Thus, in this paper, we conducted a systematic review of safety-critical scenarios between AVs and VRUs. We identified 39 papers in the literature and typical safety-critical scenarios between AVs and VRUs. They were further divided into three categories, including human factors, environmental factors, and vehicle factors. We then discussed the development, challenges, and possible solutions for each category. In order to further improve the safety of VRUs when interacting with AVs, multiple stakeholders should work together to 1) improve AI and sensor technologies and vehicle automation, 2) redesign the current transportation infrastructure, 3) design effective communication technologies and interfaces between vehicles and between vehicles and VRUs, and 4) design effective simulation and testing methods to support and evaluate both infrastructure and technologies.

Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018

Traffic Injury Prevention, Jan 31, 2022

Abstract Objective Reduced visibility for both drivers and pedestrians is a key factor underlying... more Abstract Objective Reduced visibility for both drivers and pedestrians is a key factor underlying the higher risk of vehicle-pedestrian collisions in dark conditions. This study investigated the extent to which pedestrians adjust for the higher risk of road crossing at night by comparing daytime and nighttime pedestrian road crossing using an immersive virtual environment. Method Participants physically crossed a single lane of continuous traffic in an immersive pedestrian simulator. Participants were randomly assigned to either the daytime or the nighttime lighting condition. The primary measures were the size of the gap selected for crossing and the timing of crossing motions relative to the gap. Results The results showed that there were no significant differences in gap selection or movement timing in daytime vs. nighttime lighting conditions. However, there was a marginal increase in the time to spare after crossing the road when crossing in the dark, likely due to an accumulation of small differences in gap choices and movement timing. Conclusion This study suggests that pedestrians do not adjust their road crossing to account for greater risk at night. As such, this study adds to our understanding of the potential risk factors for pedestrian injuries and fatalities in nighttime conditions.

SAE technical paper series, Apr 11, 2023

Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2020

During car-following, drivers respond to the braking or deceleration of a leading vehicle based o... more During car-following, drivers respond to the braking or deceleration of a leading vehicle based on their perceived threshold for gap, relative speed, change of the gap, time-to-collision, etc. These measures are widely used to estimate drivers’ response time to the braking of a vehicle ahead. However, it is not clear if their response is driven only by absolute thresholds or also through a comparison of the current situation with any baseline situation to which they have just been exposed. This research explored drivers’ braking response to a lead vehicle’s braking using naturalistic driving data. Two hundred and ninety-six braking events were analyzed. It was found that measures adjusted from a baseline (when the lead vehicle’s brake lights were illuminated) were more important for estimating drivers’ response time than the measures on the absolute thresholds. Predictors adjusted according to the baselines are suggested for better prediction of drivers’ response time.

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

This paper shows the scenarios in light vehicle crashes involving pedestrians and bicyclists in t... more This paper shows the scenarios in light vehicle crashes involving pedestrians and bicyclists in the United States from 2000 to 2013. NHTSA's NASS GES and FARS dataset were analyzed to determine scenarios and associated factors. Among the key findings are: 6% of total injury crashes and 13.3% of total fatal crashes were pedestrian/bicyclist related, which resulted in an average 3600 pedestrian fatalities per year, 570 bicyclist fatalities per year, 57034 pedestrian injuries per year and 43240 bicyclist injuries per year. Crashes involving older pedestrians and bicyclists (age 70+) often were more severe than other groups. More male than females were involved in both pedestrian and bicyclist related fatal and injury crashes, as either driver or road user. Most crashes happened under no adverse weather conditions, on dry principle arterial roads and at non-junction location. Daylight condition had the highest bicyclist crash rates and pedestrian injury crash rates while fatal pedestrian rates were much higher in dark but lighted condition and dark condition. Pedestrians and bicyclists were more at risk in urban areas in part due to the large number of pedestrian and vehicle activity in that area. Pedestrian were more likely to be killed in a crash between 18:00 and 21:00 and in cold season (during October to January). More bicyclist crashes happened in summer time. Before crashes happened, majority of vehicles were going straight, while more pedestrians were jaywalking/running and most of bicyclists were failing to yield. About 21% of total bicyclist related crash fatalities and 26% of total pedestrian related crash fatalities had alcohol involvement. This number may even higher since alcohol data are often missing. This analysis is quite valuable for design and evaluation of in-vehicle pedestrian and bicyclist detection system.

Traffic Injury Prevention, Sep 1, 2021

Abstract Objective Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a com... more Abstract Objective Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a combination of radar and cameras to detect pedestrians and automatically apply braking to prevent or mitigate an impending collision. However, these current sensor systems might have a restricted field-of-view (FOV) which may not detect all pedestrians. Advanced sensors like LiDAR can have a wider FOV that may substantially help improve detection. The objective of this study was to determine the influence of FOV and range on the effectiveness of P-AEB systems to determine the potential benefit of advanced sensors. Methods This study utilized vehicle-pedestrian crashes from the Pedestrian Crash Data Study (PCDS) to calculate pre-crash pedestrian and vehicle trajectories. A computational model was then applied to simulate the crash with a hypothetical P-AEB system. The model was designed to be able to vary the system’s field-of-view (FOV), range, time-to-collision of activation, and system latency. In this study we estimated how the FOV and range of advanced sensors could affect P-AEB system effectiveness at avoiding crashes and reducing impact speed. Sensor range was varied from 25 − 100 m and sensor FOV was varied from ±10° to ±90°. Results Sensors simulated with a range of 50 m or greater performed only approximately 1% better than with a 25 m range. Field-of-view had a larger effect on estimated system avoidance capabilities with a ± 10° FOV sensor estimated to avoid 46-47% of collisions compared to 91-92% for a ± 90° FOV sensor. The system was able to avoid a greater percentage of cases in which the vehicle was traveling straight at sensor FOVs of ±30° and below. Among the unavoided crashes with a sensor FOV of ±90°, the average impact velocity using a 100 m range sensor was 7.4 m/s which was 3.1 m/s lower than a 25 m range sensor. Conclusions Sensor ranges above 25 m were not found to significantly affect estimated crash avoidance potential, but had a small effect on impact mitigation. Sensor FOV had a larger effect on crash avoidance up to a FOV of ±60° with little additional benefit at larger FOVs.

SAE technical paper series, Apr 3, 2018

Intersection crashes are among the most frequent and lethal crash modes in the United States. Acc... more Intersection crashes are among the most frequent and lethal crash modes in the United States. Accounting for over one-third of all intersection crashes, straight crossing path (SCP) crashes are the most common intersection crash mode. Intersection Advanced Driver Assistance Systems (I-ADAS) have the potential to prevent SCP crashes by detecting imminent collisions and either alerting the driver and/or taking autonomous crash avoidance action. The objective of this study was to estimate how many SCP intersection crashes could be potentially prevented in the U.S. if every vehicle was equipped with I-ADAS. Three steps were performed in this study. First, a simulation case set was generated from 459 real world SCP intersection crashes collected as part of NHTSA's National Motor Vehicle Crash Causation Survey (NMVCCS) database. Second, the pre-crash kinematics of each vehicle was reconstructed using information from the crash investigation, pre-crash driver models, and reconstructed impact speeds. Third, the crashes were simulated as if both vehicles had been equipped with I-ADAS. Three critical time-to-collision (TTC) thresholds were evaluated in this study, including 2.0, 2.5, and 3.0 seconds. The model predicted that 19% to 35% of all SCP crashes have the potential to be prevented if all vehicles in the U.S. were equipped with I-ADAS. Nearly twice as many crashes were predicted to be prevented if a TTC threshold of 3.0 s was used rather than 2.0 s. When at least one of the vehicles stopped prior to entering the intersection, the model estimated that 24% to 49% of crashes have the potential to be prevented by I-ADAS. In contrast, when neither vehicle stopped, the model estimates that 13% to 17% of crashes could potentially be prevented. It is important to note that the model makes several assumptions that represent a “best case scenario” for I-ADAS. These results have important implications for designers, consumers, and regulatory agencies.

SAE technical paper series, Apr 14, 2015

Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019

Traffic Injury Prevention, Oct 12, 2020

One of the challenges of lane departure warning (LDW) systems is to differentiate between normal ... more One of the challenges of lane departure warning (LDW) systems is to differentiate between normal lane keeping behavior and lane change events in which drivers simply do not use the lane change indicator. Lane keeping behavior differs between drivers and often between driving scenarios, therefore a static threshold of predicting steering maneuver is not an ideal solution. The objective of the current study is to develop an adaptive method of predicting driver lane change maneuver using vehicle kinematic data. The paper presents an adaptive steering maneuver detection algorithm, which can detect the earliest indication of driver’s intent to change lanes. The overall approach was to observe the driver’s “normal” lane keeping behavior for a period of time, and seek driver lane keeping behavior which falls outside of what is “normal” for each specific event. We modeled normal driving behavior in this study using a bivariate normal distribution to continuously monitor the vehicle distance to lane boundary (DTLB) and lateral velocity measured in most production LDW systems. The results of our algorithm were validated against visual inspections of 949 randomly selected lane change events from the 100-Car Naturalistic Driving Study (NDS), in which we compared the time of driver steering initiation estimated by the algorithm against visual inspection. The comparison between algorithm results and visual inspection shows that all steering initiation in lane change events in the sample occurred within 5 seconds of lane crossing. In addition, a sensitivity analysis on the bivariate normal distribution boundary shows that the contour line representing 95% probability produced the lowest average percentage error (2%) with an average delay of 0.7 seconds between the algorithm predicted driver steering initiation time and video inspection. The resultant algorithm was deployed in a large subset of 100-Car and was able to identify the steering initiation time in a total of 53,615 lane change events. The resultant algorithm shows utility in assisting future active safety system in monitoring driver lane keeping behavior, as well as providing active safety system designers further understanding of driver action in lane change maneuvers to improve designs of LDW systems.

Accident Analysis & Prevention, Sep 1, 2021

Three-fourths of pedestrian fatalities in the U.S. occur in the dark (National Center for Statist... more Three-fourths of pedestrian fatalities in the U.S. occur in the dark (National Center for Statistics and Analysis, 2020). Adaptive Headlight Systems (AHS) offer the potential to address this problem by improving the visibility of pedestrians for drivers and alerting pedestrians to approaching vehicles. The goal of this study was to investigate how pedestrians respond to different types of AHS. We conducted a mixed factor experiment with 106 college-age adults using a large-screen pedestrian simulator. The task for participants was to cross a stream of continuous traffic without colliding with a vehicle. There were four AHS treatment conditions that differed in the color (white or red) and timing of an icon projected on the roadway in front the participant as an AHS vehicle approached. Participants in the treatment conditions encountered a mix of AHS and non-AHS vehicles. There was also a control condition in which participants encountered only non-AHS vehicles. We found that the color and the timing of the icon projected on the roadway influenced the size of the gaps crossed. Participants in the red icon with early onset condition chose the largest gaps for crossing. An unexpected outcome was that participants in the AHS treatment conditions chose larger gaps even when crossing in front of non-AHS vehicles, suggesting that experiences with AHS vehicles generalized to non-AHS vehicles. We conclude that AHS can have a significant, positive impact on pedestrian road-crossing safety.

SAE technical paper series, Apr 3, 2018

Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that have the ... more Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that have the potential to help prevent/mitigate crashes and injuries in intersection crashes. I-ADAS may use side-looking sensors, e.g. radar and lidar, in order to detect potential collisions with vehicles from crossing paths. The success of I-ADAS depends on the range and azimuth capabilities of these sensors. In order to specify the capabilities of sensors for an I-ADAS, designers need a distribution of range and azimuth between vehicles as they enter intersections prior to crashes. This study generated range and azimuth distributions using crash data from the National Motor Vehicle Crash Causation Survey (NMVCCS) for vehicles just prior to entering the intersection in straight crossing paths (SCP) crashes. Using the reconstructions and specifications in existing radar technology, the potential crash mitigation benefits of this technology were determined. Three radar-based I-ADAS were analyzed using published sensor specifications. The sensors included a wide beam, intermediate beam, and narrow beam. The wide beam I-ADAS was found to detect 20.3% of oncoming vehicles, the intermediate beam was found to detect 89.2% of oncoming vehicles, and the narrow beam was found to detect 98.3% of oncoming vehicles. The results indicate that a narrow beam I-ADAS is the most capable because of its long range detection ability. These results have practical relevance for the design and implementation of I-ADAS.

Advances in transportation studies, 2020

Proceedings of the 2018 International IRCOBI Conference on the Biomechanics of Injury, 2018

arXiv (Cornell University), May 18, 2023

Automated vehicles (AVs) are of great potential in reducing crashes on the road. However, it is s... more Automated vehicles (AVs) are of great potential in reducing crashes on the road. However, it is still complicated to eliminate all the possible accidents, especially those with vulnerable road users (VRUs), who are among the greater risk than vehicle occupants in traffic accidents. Thus, in this paper, we conducted a systematic review of safety-critical scenarios between AVs and VRUs. We identified 39 papers in the literature and typical safety-critical scenarios between AVs and VRUs. They were further divided into three categories, including human factors, environmental factors, and vehicle factors. We then discussed the development, challenges, and possible solutions for each category. In order to further improve the safety of VRUs when interacting with AVs, multiple stakeholders should work together to 1) improve AI and sensor technologies and vehicle automation, 2) redesign the current transportation infrastructure, 3) design effective communication technologies and interfaces between vehicles and between vehicles and VRUs, and 4) design effective simulation and testing methods to support and evaluate both infrastructure and technologies.

Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018

Traffic Injury Prevention, Jan 31, 2022

Abstract Objective Reduced visibility for both drivers and pedestrians is a key factor underlying... more Abstract Objective Reduced visibility for both drivers and pedestrians is a key factor underlying the higher risk of vehicle-pedestrian collisions in dark conditions. This study investigated the extent to which pedestrians adjust for the higher risk of road crossing at night by comparing daytime and nighttime pedestrian road crossing using an immersive virtual environment. Method Participants physically crossed a single lane of continuous traffic in an immersive pedestrian simulator. Participants were randomly assigned to either the daytime or the nighttime lighting condition. The primary measures were the size of the gap selected for crossing and the timing of crossing motions relative to the gap. Results The results showed that there were no significant differences in gap selection or movement timing in daytime vs. nighttime lighting conditions. However, there was a marginal increase in the time to spare after crossing the road when crossing in the dark, likely due to an accumulation of small differences in gap choices and movement timing. Conclusion This study suggests that pedestrians do not adjust their road crossing to account for greater risk at night. As such, this study adds to our understanding of the potential risk factors for pedestrian injuries and fatalities in nighttime conditions.

SAE technical paper series, Apr 11, 2023

Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2020

During car-following, drivers respond to the braking or deceleration of a leading vehicle based o... more During car-following, drivers respond to the braking or deceleration of a leading vehicle based on their perceived threshold for gap, relative speed, change of the gap, time-to-collision, etc. These measures are widely used to estimate drivers’ response time to the braking of a vehicle ahead. However, it is not clear if their response is driven only by absolute thresholds or also through a comparison of the current situation with any baseline situation to which they have just been exposed. This research explored drivers’ braking response to a lead vehicle’s braking using naturalistic driving data. Two hundred and ninety-six braking events were analyzed. It was found that measures adjusted from a baseline (when the lead vehicle’s brake lights were illuminated) were more important for estimating drivers’ response time than the measures on the absolute thresholds. Predictors adjusted according to the baselines are suggested for better prediction of drivers’ response time.

2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015

This paper shows the scenarios in light vehicle crashes involving pedestrians and bicyclists in t... more This paper shows the scenarios in light vehicle crashes involving pedestrians and bicyclists in the United States from 2000 to 2013. NHTSA's NASS GES and FARS dataset were analyzed to determine scenarios and associated factors. Among the key findings are: 6% of total injury crashes and 13.3% of total fatal crashes were pedestrian/bicyclist related, which resulted in an average 3600 pedestrian fatalities per year, 570 bicyclist fatalities per year, 57034 pedestrian injuries per year and 43240 bicyclist injuries per year. Crashes involving older pedestrians and bicyclists (age 70+) often were more severe than other groups. More male than females were involved in both pedestrian and bicyclist related fatal and injury crashes, as either driver or road user. Most crashes happened under no adverse weather conditions, on dry principle arterial roads and at non-junction location. Daylight condition had the highest bicyclist crash rates and pedestrian injury crash rates while fatal pedestrian rates were much higher in dark but lighted condition and dark condition. Pedestrians and bicyclists were more at risk in urban areas in part due to the large number of pedestrian and vehicle activity in that area. Pedestrian were more likely to be killed in a crash between 18:00 and 21:00 and in cold season (during October to January). More bicyclist crashes happened in summer time. Before crashes happened, majority of vehicles were going straight, while more pedestrians were jaywalking/running and most of bicyclists were failing to yield. About 21% of total bicyclist related crash fatalities and 26% of total pedestrian related crash fatalities had alcohol involvement. This number may even higher since alcohol data are often missing. This analysis is quite valuable for design and evaluation of in-vehicle pedestrian and bicyclist detection system.

Traffic Injury Prevention, Sep 1, 2021

Abstract Objective Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a com... more Abstract Objective Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a combination of radar and cameras to detect pedestrians and automatically apply braking to prevent or mitigate an impending collision. However, these current sensor systems might have a restricted field-of-view (FOV) which may not detect all pedestrians. Advanced sensors like LiDAR can have a wider FOV that may substantially help improve detection. The objective of this study was to determine the influence of FOV and range on the effectiveness of P-AEB systems to determine the potential benefit of advanced sensors. Methods This study utilized vehicle-pedestrian crashes from the Pedestrian Crash Data Study (PCDS) to calculate pre-crash pedestrian and vehicle trajectories. A computational model was then applied to simulate the crash with a hypothetical P-AEB system. The model was designed to be able to vary the system’s field-of-view (FOV), range, time-to-collision of activation, and system latency. In this study we estimated how the FOV and range of advanced sensors could affect P-AEB system effectiveness at avoiding crashes and reducing impact speed. Sensor range was varied from 25 − 100 m and sensor FOV was varied from ±10° to ±90°. Results Sensors simulated with a range of 50 m or greater performed only approximately 1% better than with a 25 m range. Field-of-view had a larger effect on estimated system avoidance capabilities with a ± 10° FOV sensor estimated to avoid 46-47% of collisions compared to 91-92% for a ± 90° FOV sensor. The system was able to avoid a greater percentage of cases in which the vehicle was traveling straight at sensor FOVs of ±30° and below. Among the unavoided crashes with a sensor FOV of ±90°, the average impact velocity using a 100 m range sensor was 7.4 m/s which was 3.1 m/s lower than a 25 m range sensor. Conclusions Sensor ranges above 25 m were not found to significantly affect estimated crash avoidance potential, but had a small effect on impact mitigation. Sensor FOV had a larger effect on crash avoidance up to a FOV of ±60° with little additional benefit at larger FOVs.

SAE technical paper series, Apr 3, 2018

Intersection crashes are among the most frequent and lethal crash modes in the United States. Acc... more Intersection crashes are among the most frequent and lethal crash modes in the United States. Accounting for over one-third of all intersection crashes, straight crossing path (SCP) crashes are the most common intersection crash mode. Intersection Advanced Driver Assistance Systems (I-ADAS) have the potential to prevent SCP crashes by detecting imminent collisions and either alerting the driver and/or taking autonomous crash avoidance action. The objective of this study was to estimate how many SCP intersection crashes could be potentially prevented in the U.S. if every vehicle was equipped with I-ADAS. Three steps were performed in this study. First, a simulation case set was generated from 459 real world SCP intersection crashes collected as part of NHTSA's National Motor Vehicle Crash Causation Survey (NMVCCS) database. Second, the pre-crash kinematics of each vehicle was reconstructed using information from the crash investigation, pre-crash driver models, and reconstructed impact speeds. Third, the crashes were simulated as if both vehicles had been equipped with I-ADAS. Three critical time-to-collision (TTC) thresholds were evaluated in this study, including 2.0, 2.5, and 3.0 seconds. The model predicted that 19% to 35% of all SCP crashes have the potential to be prevented if all vehicles in the U.S. were equipped with I-ADAS. Nearly twice as many crashes were predicted to be prevented if a TTC threshold of 3.0 s was used rather than 2.0 s. When at least one of the vehicles stopped prior to entering the intersection, the model estimated that 24% to 49% of crashes have the potential to be prevented by I-ADAS. In contrast, when neither vehicle stopped, the model estimates that 13% to 17% of crashes could potentially be prevented. It is important to note that the model makes several assumptions that represent a “best case scenario” for I-ADAS. These results have important implications for designers, consumers, and regulatory agencies.

SAE technical paper series, Apr 14, 2015

Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019

Traffic Injury Prevention, Oct 12, 2020

One of the challenges of lane departure warning (LDW) systems is to differentiate between normal ... more One of the challenges of lane departure warning (LDW) systems is to differentiate between normal lane keeping behavior and lane change events in which drivers simply do not use the lane change indicator. Lane keeping behavior differs between drivers and often between driving scenarios, therefore a static threshold of predicting steering maneuver is not an ideal solution. The objective of the current study is to develop an adaptive method of predicting driver lane change maneuver using vehicle kinematic data. The paper presents an adaptive steering maneuver detection algorithm, which can detect the earliest indication of driver’s intent to change lanes. The overall approach was to observe the driver’s “normal” lane keeping behavior for a period of time, and seek driver lane keeping behavior which falls outside of what is “normal” for each specific event. We modeled normal driving behavior in this study using a bivariate normal distribution to continuously monitor the vehicle distance to lane boundary (DTLB) and lateral velocity measured in most production LDW systems. The results of our algorithm were validated against visual inspections of 949 randomly selected lane change events from the 100-Car Naturalistic Driving Study (NDS), in which we compared the time of driver steering initiation estimated by the algorithm against visual inspection. The comparison between algorithm results and visual inspection shows that all steering initiation in lane change events in the sample occurred within 5 seconds of lane crossing. In addition, a sensitivity analysis on the bivariate normal distribution boundary shows that the contour line representing 95% probability produced the lowest average percentage error (2%) with an average delay of 0.7 seconds between the algorithm predicted driver steering initiation time and video inspection. The resultant algorithm was deployed in a large subset of 100-Car and was able to identify the steering initiation time in a total of 53,615 lane change events. The resultant algorithm shows utility in assisting future active safety system in monitoring driver lane keeping behavior, as well as providing active safety system designers further understanding of driver action in lane change maneuvers to improve designs of LDW systems.

Accident Analysis & Prevention, Sep 1, 2021

Three-fourths of pedestrian fatalities in the U.S. occur in the dark (National Center for Statist... more Three-fourths of pedestrian fatalities in the U.S. occur in the dark (National Center for Statistics and Analysis, 2020). Adaptive Headlight Systems (AHS) offer the potential to address this problem by improving the visibility of pedestrians for drivers and alerting pedestrians to approaching vehicles. The goal of this study was to investigate how pedestrians respond to different types of AHS. We conducted a mixed factor experiment with 106 college-age adults using a large-screen pedestrian simulator. The task for participants was to cross a stream of continuous traffic without colliding with a vehicle. There were four AHS treatment conditions that differed in the color (white or red) and timing of an icon projected on the roadway in front the participant as an AHS vehicle approached. Participants in the treatment conditions encountered a mix of AHS and non-AHS vehicles. There was also a control condition in which participants encountered only non-AHS vehicles. We found that the color and the timing of the icon projected on the roadway influenced the size of the gaps crossed. Participants in the red icon with early onset condition chose the largest gaps for crossing. An unexpected outcome was that participants in the AHS treatment conditions chose larger gaps even when crossing in front of non-AHS vehicles, suggesting that experiences with AHS vehicles generalized to non-AHS vehicles. We conclude that AHS can have a significant, positive impact on pedestrian road-crossing safety.

SAE technical paper series, Apr 3, 2018

Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that have the ... more Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that have the potential to help prevent/mitigate crashes and injuries in intersection crashes. I-ADAS may use side-looking sensors, e.g. radar and lidar, in order to detect potential collisions with vehicles from crossing paths. The success of I-ADAS depends on the range and azimuth capabilities of these sensors. In order to specify the capabilities of sensors for an I-ADAS, designers need a distribution of range and azimuth between vehicles as they enter intersections prior to crashes. This study generated range and azimuth distributions using crash data from the National Motor Vehicle Crash Causation Survey (NMVCCS) for vehicles just prior to entering the intersection in straight crossing paths (SCP) crashes. Using the reconstructions and specifications in existing radar technology, the potential crash mitigation benefits of this technology were determined. Three radar-based I-ADAS were analyzed using published sensor specifications. The sensors included a wide beam, intermediate beam, and narrow beam. The wide beam I-ADAS was found to detect 20.3% of oncoming vehicles, the intermediate beam was found to detect 89.2% of oncoming vehicles, and the narrow beam was found to detect 98.3% of oncoming vehicles. The results indicate that a narrow beam I-ADAS is the most capable because of its long range detection ability. These results have practical relevance for the design and implementation of I-ADAS.

Advances in transportation studies, 2020

Proceedings of the 2018 International IRCOBI Conference on the Biomechanics of Injury, 2018