Can non-crash naturalistic driving data be an alternative to crash data for use in virtual assessment of the safety performance of automated emergency braking systems?

INTRODUCTION: Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS). METHOD: Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations - simulating the time-series crash data from each data source. RESULTS: Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data. CONCLUSIONS: If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment. PRACTICAL APPLICATIONS: Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer.

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts-especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car-to-cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists' fatalities by 53-96% and serious injuries by 43-94%, depending on the driver response model. The shorter the driver's reaction time and the larger the driver's deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW's great potential to avoid crashes and reduce injuries in car-to-cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures.

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes.

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior. The resulting models are fitted to time-series data from real-world naturalistic rear-end crashes and near-crashes. A stringent parameterization and model selection procedure is presented, based on particle swarm optimization and maximum likelihood estimation. A major contribution of this paper is the resulting first-ever fit of a computational model of human braking to real near-crash and crash behavior data. The model selection results also permit novel conclusions regarding behavior and accident causation: Firstly, the results indicate that drivers have partial visual looming perception during off-road glances; that is, evidence for braking is collected, albeit at a slower pace, while the driver is looking away from the forward roadway. Secondly, the results suggest that an important causation factor in crashes without off-road glances may be a reduced responsiveness to visual looming, possibly associated with cognitive driver state (e.g., drowsiness or erroneous driver expectations). It is also demonstrated that a model parameterized on less-critical data, such as near-crashes, may also accurately reproduce driver behavior in highly critical situations, such as crashes.

Evaluating automated emergency braking performance in simulated car-to-two-wheeler crashes in China: A comparison between C-NCAP tests and in-depth crash data.

Automated Emergency Braking (AEB) is an effective way to prevent crashes from happening or mitigate their severity. Because riders of two-wheelers (TWs) are among the most vulnerable road users, New Car Assessment Programs, like the China New Car Assessment Program (C-NCAP), have recently introduced test scenarios for the assessment of AEB for cars encountering TWs (TW-AEB). The main aim of this study was to determine how well two different C-NCAP test scenario datasets reflect real-world crash scenarios for the purpose of assessing TW-AEB performance. We used virtual counterfactual simulations to determine whether the hypothetical TW-AEB's performance, when applied to the two C-NCAP datasets, was similar to its performance when applied to a set of reconstructed car-to-TW crashes representing real-world crashes. The test datasets were the current C-NCAP scenario set and a proposed C-NCAP scenario set; the real-world crash dataset comprised 113 reconstructed crashes from the Shanghai United Road Traffic Safety Scientific Research Center database (SHUFO). The performances were compared with respect to crash avoidance rate and the characteristics of the remaining crashes. A substantially higher proportion of crashes was avoided in the current C-NCAP scenario set than in the other two (with the sensor field of view (FoV) set to 70° and the activation time to 1.1 s TTC). In fact, with these parameter settings, no crashes remained in the current C-NCAP scenarios, while only 37% and 46% of the crashes in the proposed C-NCAP scenario set and SHUFO crash set were avoided, respectively. Our findings show that TW-AEB systems which are optimized for the current C-NCAP test scenarios are likely to provide benefits in real-world crashes. However, including additional test scenarios which reflect real-world crash situations more accurately would likely lead to a higher correlation between C-NCAP scores and real-world TW-AEB performance. In particular, we recommend the introduction of longitudinal same-direction scenarios with the car or TW turning and perpendicular scenarios with high TW traveling speed, in future C-NCAP releases. Inclusion of these scenarios in C-NCAP might reward improvements of future TW-AEBs toward systems that can save more lives. Furthermore, our study shows that there is likely to be a substantial number of crashes with an impact speed higher than 40 km/h still remaining even after the widespread application of TW-AEB. Therefore, passive safety for TW riders on Chinese roads will be still needed.

Detection and response to critical lead vehicle deceleration events with peripheral vision: Glance response times are independent of visual eccentricity.

Studies show high correlations between drivers' off-road glance duration or pattern and the frequency of crashes. Understanding drivers' use of peripheral vision to detect and react to threats is essential to modelling driver behavior and, eventually, preventing crashes caused by visual distraction. A between-group experiment with 83 participants was conducted in a high-fidelity driving simulator. Each driver in the experiment was exposed to an unexpected, critical, lead vehicle deceleration, when performing a self-paced, visual-manual, tracking task at different horizontal visual eccentricity angles (12°, 40° and 60°). The effect of visual eccentricity on threat detection, glance and brake response times was analyzed. Contrary to expectations, the driver glance response time was found to be independent of the eccentricity angle of the secondary task. However, the brake response time increased with increasing task eccentricity, when measured from the driver's gaze redirection to the forward roadway. High secondary task eccentricity was also associated with a low threat detection rate and drivers were predisposed to perform frequent on-road check glances while executing the task. These observations indicate that drivers use peripheral vision to collect evidence for braking during off-road glances. The insights will be used in extensions of existing driver models for virtual testing of critical longitudinal situations, to improve the representativeness of the simulation results.

A clustering approach to developing car-to-two-wheeler test scenarios for the assessment of Automated Emergency Braking in China using in-depth Chinese crash data.

Two-wheeled vehicles (motorized and non-motorized, referred to as TWs) are an important part of the transport system in China. They also represent an important challenge for road safety, with many TW user fatalities and injuries every year. Recently, active safety systems for cars, such as Automated Emergency Braking (AEB), promise to reduce road traffic fatalities and injuries. For these systems to work effectively, it is necessary to understand and define the complex traffic scenarios to be addressed. The aim of this study is to contribute to the development of test procedures for AEB specifically, drawing on the China In-Depth Accident Study (CIDAS) data from July 2011 to February 2016 to describe typical scenarios for crashes between cars and TWs by means of cluster analysis. In total, 672 car-to-TW crashes were extracted. The data was clustered according to five main crash characteristics: time of crash, view obstruction, pre-crash driving behavior of the car driver and the TW driver, and relative moving direction. The analysis resulted in six car-to-TW crash scenarios typical of China. In three scenarios the car and the TW travel perpendicularly to each other before the crash, in two they travel in the same direction, and in one they travel in opposite directions. Further, each scenario can be described with three characteristics (the road speed limit, the TW's first contact point on the car, and the car's first contact point on the TW) that can be included in an AEB test suite. Some scenarios were similar to those in the Euro New Car Assessment Program (Euro NCAP). For example, in one, a TW moving straight ahead was hit by a car moving perpendicularly, and in the other the car hit a TW traveling in the same direction. Both occurred in daytime, without a visual obstruction. However, in contrast to the Euro NCAP, typical scenarios in China included night-time scenarios, scenarios where the car or the TW was turning, and those in which the TW was hidden from the car by an obstruction. The results contribute to a proposed novel AEB test suite with realistic scenarios specific to China.

How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving.

Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have identified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85-5.00 times more crashes than baseline driving.

Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders.

INTRODUCTION: In the last 30years, China has undergone a dramatic increase in vehicle ownership and a resulting escalation in the number of road crashes. Although crash figures are decreasing today, they remain high; it is therefore important to investigate crash causation mechanisms to further improve road safety in China. METHOD: To shed more light on the topic, naturalistic driving data was collected in Shanghai as part of the evaluation of a behavior-based safety service. The data collection included instrumenting 47 vehicles belonging to a commercial fleet with data acquisition systems. From the overall sample, 91 rear-end crash or near-crash (CNC) events, triggered by 24 drivers, were used in the analysis. The CNC were annotated by three researchers, through an expert assessment methodology based on videos and kinematic variables. RESULTS: The results show that the main factor behind the rear-end CNC was the adoption of very small safety margins. In contrast to results from previous studies in the US, the following vehicles' drivers typically had their eyes on the road and reacted quickly in response to the evolving conflict in most events. When delayed reactions occurred, they were mainly due to driving-related visual scanning mismatches (e.g., mirror checks) rather than visual distraction. Finally, the study identified four main conflict scenarios that represent the typical development of rear-end conflicts in this data. CONCLUSIONS: The findings of this study have several practical applications, such as informing the specifications of in-vehicle safety measures and automated driving and providing input into the design of coaching/training procedures to improve the driving habits of drivers.

Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems.

As the development and deployment of in-vehicle intelligent safety systems (ISS) for crash avoidance and mitigation have rapidly increased in the last decades, the need to evaluate their prospective safety benefits before introduction has never been higher. Counterfactual simulations using relevant mathematical models (for vehicle dynamics, sensors, the environment, ISS algorithms, and models of driver behavior) have been identified as having high potential. However, although most of these models are relatively mature, models of driver behavior in the critical seconds before a crash are still relatively immature. There are also large conceptual differences between different driver models. The objective of this paper is, firstly, to demonstrate the importance of the choice of driver model when counterfactual simulations are used to evaluate two ISS: Forward collision warning (FCW), and autonomous emergency braking (AEB). Secondly, the paper demonstrates how counterfactual simulations can be used to perform sensitivity analyses on parameter settings, both for driver behavior and ISS algorithms. Finally, the paper evaluates the effect of the choice of glance distribution in the driver behavior model on the safety benefit estimation. The paper uses pre-crash kinematics and driver behavior from 34 rear-end crashes from the SHRP2 naturalistic driving study for the demonstrations. The results for FCW show a large difference in the percent of avoided crashes between conceptually different models of driver behavior, while differences were small for conceptually similar models. As expected, the choice of model of driver behavior did not affect AEB benefit much. Based on our results, researchers and others who aim to evaluate ISS with the driver in the loop through counterfactual simulations should be sure to make deliberate and well-grounded choices of driver models: the choice of model matters.

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies.

Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined "hazard onset", but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario.

Chunking: a procedure to improve naturalistic data analysis.

Every year, traffic accidents are responsible for more than 1,000,000 fatalities worldwide. Understanding the causes of traffic accidents and increasing safety on the road are priority issues for both legislators and the automotive industry. Recently, in Europe, the US and Japan, significant public funding has been allocated for performing large-scale naturalistic driving studies to better understand accident causation and the impact of safety systems on traffic safety. The data provided by these naturalistic driving studies has never been available before in this quantity and comprehensiveness and it promises to support a wide variety of data analyses. The volume and variety of the data also pose substantial challenges that demand new data reduction and analysis techniques. This paper presents a general procedure for the analysis of naturalistic driving data called chunking that can support many of these analyses by increasing their robustness and sensitivity. Chunking divides data into equivalent, elementary chunks of data to facilitate a robust and consistent calculation of parameters. This procedure was applied, as an example, to naturalistic driving data from the SeMiFOT study in Sweden and compared with alternative procedures from past studies in order to show its advantages and rationale in a specific example. Our results show how to apply the chunking procedure and how chunking can help avoid bias from data segments with heterogeneous durations (typically obtained from SQL queries). Finally, this paper shows how chunking can increase the robustness of parameter calculation, statistical sensitivity, and create a solid basis for further data analyses.

Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM).

To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.