John M. Scanlon

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes. Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicles calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver. Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle. Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.

Potential of intersection driver assistance systems to mitigate straight crossing path crashes using U.S. nationally representative crash data

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.

Preliminary potential crash prevention estimates for an Intersection Advanced Driver Assistance System in straight crossing path crashes

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 NHTSAs 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.

Predicting Crash-Relevant Violations at Stop Sign–Controlled Intersections for the Development of an Intersection Driver Assistance System

ABSTRACT Objective: Intersection crashes resulted in over 5,000 fatalities in the United States in 2014. Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that seek to help drivers safely traverse intersections. I-ADAS uses onboard sensors to detect oncoming vehicles and, in the event of an imminent crash, can either alert the driver or take autonomous evasive action. The objective of this study was to develop and evaluate a predictive model for detecting whether a stop sign violation was imminent. Methods: Passenger vehicle intersection approaches were extracted from a data set of typical driver behavior (100-Car Naturalistic Driving Study) and violations (event data recorders downloaded from real-world crashes) and were assigned weighting factors based on real-world frequency. A k-fold cross-validation procedure was then used to develop and evaluate 3 hypothetical stop sign warning algorithms (i.e., early, intermediate, and delayed) for detecting an impending violation during the intersection approach. Violation detection models were developed using logistic regression models that evaluate likelihood of a violation at various locations along the intersection approach. Two potential indicators of driver intent to stop—that is, required deceleration parameter (RDP) and brake application—were used to develop the predictive models. The earliest violation detection opportunity was then evaluated for each detection algorithm in order to (1) evaluate the violation detection accuracy and (2) compare braking demand versus maximum braking capabilities. Results: A total of 38 violating and 658 nonviolating approaches were used in the analysis. All 3 algorithms were able to detect a violation at some point during the intersection approach. The early detection algorithm, as designed, was able to detect violations earlier than all other algorithms during the intersection approach but gave false alarms for 22.3% of approaches. In contrast, the delayed detection algorithm sacrificed some time for detecting violations but was able to substantially reduce false alarms to only 3.3% of all nonviolating approaches. Given good surface conditions (maximum braking capabilities = 0.8 g) and maximum effort, most drivers (55.3–71.1%) would be able to stop the vehicle regardless of the detection algorithm. However, given poor surface conditions (maximum braking capabilities = 0.4 g), few drivers (10.5–26.3%) would be able to stop the vehicle. Automatic emergency braking (AEB) would allow for early braking prior to driver reaction. If equipped with an AEB system, the results suggest that, even for the poor surface conditions scenario, over one half (55.3–65.8%) of the vehicles could have been stopped. Conclusions: This study demonstrates the potential of I-ADAS to incorporate a stop sign violation detection algorithm. Repeating the analysis on a larger, more extensive data set will allow for the development of a more comprehensive algorithm to further validate the findings.

Models of Driver Acceleration Behavior Prior to Real-World Intersection Crashes

Drivers involved in intersection collisions are at high risk of serious or fatal injury. Intersection advanced driver assistance systems (I-ADAS) are emerging active safety systems designed to help drivers safely traverse intersections. The effectiveness of I-ADAS is expected to be greatly dependent on pre-crash vehicle acceleration during intersection traversals. The objective of this paper was to develop pre-crash acceleration models for non-turning drivers involved in straight crossing path crashes and left-turning drivers in left turn across path opposite direction and lateral direction crashes. This paper used 348 event data recorder pre-crash records taken from crashes investigated as part of the National Automotive Sampling System/Crashworthiness Data System. The acceleration models generated from this pre-crash data were evaluated using a leave-one-out cross-validation procedure. Previously developed non-crash models from the literature were compared with the pre-crash models. Our hypothesis was that drivers involved in crashes would accelerate more aggressively than the “typical” driving population. This result suggests that drivers in pre-crash scenarios tend to accelerate more aggressively than drivers in normal scenarios (p<0.001). This has important implications for the design of I-ADAS. Specifically, higher acceleration results in less available time for I-ADAS to detect and respond to an imminent collision.

Lane Departure Warning and Prevention Systems in the U.S. Vehicle Fleet: Influence of Roadway Characteristics on Potential Safety Benefits

Road departure crashes account for one-tenth of all crashes but nearly one-third of all fatal crashes. Lane departure warning (LDW) and lane departure prevention (LDP) active safety systems could mitigate these crashes by warning the driver of a lane departure or automatically navigating the vehicle back into the lane. The objective of this study was to quantify the influence of certain roadway characteristics on the effectiveness of LDW and LDP systems within the U.S. vehicle fleet. This study used 478 real-world drift-out-of-lane road departure crashes and simulated them as if the vehicles had been equipped with LDW or LDP systems. The simulations were then repeated as if (a) all of the roadways had lane markings, (b) the roadway shoulders were expanded, and (c) lane markings were present and the shoulder widths were expanded. With the existing roadway infrastructure, LDW and LDP were found to potentially prevent 28% to 32% of U.S. road departure crashes and 21% to 28% of cases of serious driver injury. When lane markings were added to the roadways, LDW and LDP could prevent 32% to 36% of crashes and 27% to 31% of cases of serious driver injury. When only shoulder widths were expanded, LDW and LDP could prevent 50% to 54% of crashes and 44% to 48% of cases of serious driver injury. When lane markings were present and the shoulders were expanded, LDW and LDP could prevent 72% to 78% of crashes and 60% to 65% of cases of serious driver injury. The findings of this study highlight the important influence of roadway infrastructure on the performance of LDW and LDP.

Earliest Sensor Detection Opportunity for Left Turn Across Path Opposite Direction Crashes

Left turn across path crashes with a vehicle traveling from the opposite direction (LTAP/OD) are a common and often fatal intersection crash scenario in the U.S. Intersection advanced driver assistance systems (I-ADAS) are active safety systems emerging in the vehicle fleet that are intended to help drivers safely traverse intersections. The objective of this study was to examine the earliest detection opportunity for I-ADAS in LTAP/OD intersection crashes. A total of 35 crashes were extracted for this studys analysis from the NASS/CDS crash database. EDR precrash records taken from each vehicle were then used to determine vehicle position with respect to time. Two scenarios are considered: one with and one without potential sight occlusions. The results suggest that, even if no sight obstructions are present, an I-ADAS that warns drivers of an impending collision will be greatly limited by perception–reaction time. Accordingly, systems that employ automated emergency braking are expected to be substantially more effective. Required detection distances and azimuth values are presented. The results highlight the need for careful tuning of sensor capabilities and the need to consider side-facing sensors for ensuring vehicle tracking prior to any potential collision conflict.