Braking Event Research Articles

Determination of rates of occurrence for hydroplaning events with naturalistic driving data

Introduction: The degree to which hydroplaning occurs in real-world conditions is not entirely known. Naturalistic driving data can be helpful in addressing some of the limitations of existing data sources related to the incidence of hydroplaning. Method: Data from the Second Strategic Highway Research Program Naturalistic Driving Study were leveraged to estimate the incidence of hydroplaning. Two hydroplaning detection algorithms were used for candidate hydroplaning event generation. Hard braking events were also identified and analyzed as normative comparisons to the hydroplaning events. Results: A total of 1,141 hydroplaning events were found in the naturalistic driving data and utilized for analysis, including 650 hydroplaning events that were unnoticeable by the driver based on lack of observable reactions, 13 events that were deemed to be of “critical” severity, and only 3 events that resulted in crash events during more than 30 million miles of driving. Hard braking events occurred nearly four times more often, and at comparatively lower speeds, than hydroplaning events. Observable driver reactions also differed between event types. For example, more drivers changed their posture after a hydroplaning event than after a hard braking event and drivers maintained both hands on the wheel at higher rates after experiencing a hydroplaning event than after a hard braking event. Suspension of secondary tasks during hard braking and hydroplaning events was also observed. Conclusions: Overall, these findings suggest that drivers perceive hydroplaning events as more harmful than hard braking events, despite the large discrepancy and incompatibility in how often these driving situations lead to vehicular crashes. Practical application: The findings of this research will provide vehicle and tire designers with empirical data that quantifies the important tradeoffs they must make in balancing vehicle and tire performance in wet and dry environments, and in tradeoffs related to tire wear performance and grip.

Discovering the Power of External Human-Machine Interface: An EEG Study on the Driving Anger Regulation

Driving with anger can result in hazardous driving behaviors, often arising from inadequate communication among Traffic Participants (TPs). Currently, external Human-Machine Interface (eHMI) technology provides a novel channel for enhancing communication among TPs, yet its role in regulating driving emotions remains uncertain. This study investigates the impact of eHMI technology on the regulation of driving anger. We simulated a driving scenario in which the preceding vehicle abruptly applied brakes to elicit anger from the following driver. Three eHMI formats (Symbol, Text, and Symbol + Text) positioned at the rear of the preceding vehicle were implemented to explain the sudden braking event and regulate drivers’ emotions. Objective electroencephalogram (EEG) signals and subjective assessment data were analyzed to assess the effects of eHMIs on driving anger regulation. The results indicate that driving with eHMIs exhibited significant regulatory quality of drivers’ anger. Among the three formats, Symbol + Text eHMI exerted a notable regulatory impact, while both Symbol and Text eHMI contributed to emotional regulation. This study illustrates the positive impact of eHMI on emotion regulation in traffic environments, providing valuable backing for traffic management authorities in developing guidelines and policies, and contributing to the standardization of future eHMI technologies.

Automotive digital forensics through data and log analysis of vehicle diagnosis Android apps

Modern vehicles including smart cars have been equipped with many electronic devices such as electronic control units (ECUs), on-board diagnostics (OBD) systems, telematics and infotainment systems, gateways, sensors, etc. Because these devices create, transmit, and store a lot of digital data, modern vehicles are becoming key source of digital evidence in vehicular forensics. In addition, some dedicated mobile apps can capture driving and diagnostic data from a vehicle via a Bluetooth-enabled OBD-II scanner. In this paper, we propose a new process for effective automotive forensics. It collects and analyzes three different types of data left on an Android phone which has been connected to the OBD-II port of a vehicle via Bluetooth communication. The three types of data are OBD-II Android apps' data, Bluetooth HCI snoop log, and the main log buffer of the Android logging system. By analyzing them individually and integratedly, we find Bluetooth connection time, vehicle information, MAC address of the OBD-II scanner, vehicle velocity, sharp speeding event, sudden braking event, refueling event, and so on. We also construct a timeline of Bluetooth traffic and driving events through the timeline analysis, which can be used to determine the driver's behaviors in terms of vehicle forensics.

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.

Characterizing the effect of mind wandering on braking dynamics in partially autonomous vehicles

Partially autonomous driving systems may require the human driver to take control at any moment, yet by their design, often cause difficulty with attention management. In this preliminary study, we propose a data- and dynamics-driven approach to characterize driving performance in a partially autonomous vehicle during a manual braking event, under attentive or mind wandering states. A 10-participant experiment was completed in an advanced driving simulator. We employ a non-parametric learning technique, conditional distribution embeddings, to the driving simulator data, to evaluate likelihood of successfully completing the braking maneuver, under both attentive and mind wandering states. Our approach shows a statistically significant difference in braking profiles during mind wandering and non-mind wandering episodes for each participant. Our results reveal that heterogeneity in driving performance may have important implications for the design of autonomy that is responsive to attentional states. Data-driven tools, such as the one proposed here, may be useful in designing participant-specific alerts and warnings for control handovers and other safety-critical maneuvers, because of their potential to accommodate heterogeneous response.

COVID-19 and Driving Behavior: Which Were the Most Crucial Influencing Factors?

This paper tries to identify and investigate the most significant factors that influenced the relationship between COVID-19 pandemic metrics (i.e., COVID-19 cases, fatalities, and reproduction rate) and restrictions (i.e., stringency index and lockdown measures) with driving behavior in the entire year 2020. To that aim, naturalistic driving data for a 12-month timeframe were exploited and analyzed. The examined driving behavior variables included harsh acceleration and harsh braking event rates concerning the time period before, during, and after the lockdown measures in Greece. The harsh event rates were extracted using data obtained by a specially developed smartphone application which were transmitted to a back-end telematics platform between the 1st of January and the 31st of December, 2020. Based on the collected data, XGBoost feature analysis algorithms were deployed to obtain the most significant factors. Furthermore, a comparison among the first COVID-19 lockdown (i.e., March–May 2020), the second one (i.e., November–December 2020), and the period without COVID-19 restrictions (i.e., January–March and May–November 2020) was drawn. COVID-19 new cases and new fatalities were the most significant factors related to COVID-19 metrics impacting driving behavior. Additionally, the correlation between driving behavior with other factors (i.e., distance traveled, mobile use, driving requests, and driving during risky hours) was revealed. Furthermore, the differences and similarities of the harsh event rates between the two lockdown periods were identified. This paper tries to fill this gap in the existing literature concerning a feature analysis for the entire 2020 and including the first and second lockdown restrictions of the COVID-19 pandemic in Greece.

Modelling braking behaviour of distracted young drivers in car-following interactions: A grouped random parameters duration model with heterogeneity-in-means

Braking is an important characteristic of driving behaviour that has a direct relationship with rear-end collisions in a car-following task. Braking becomes more crucial when drivers’ cognitive workload increases because of using mobile phones whilst driving. This study, therefore, investigates and compares the effects of using mobile phones whilst driving on braking behaviour. Thirty-two young licenced drivers, evenly split by gender, faced a safety–critical event, that is, leader’s hard braking, in a car-following situation. Each participant drove the CARRS-Q Advanced Driving Simulator and was required to respond to a braking event in the simulated environment in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A random parameters duration modelling approach is employed to (i) model drivers’ braking (or deceleration) times using a parametric survival model, (ii) capture unobserved heterogeneity associated with braking times, and (iii) account for repeated experiment design. The model identifies the handheld phone condition as a random parameter whilst vehicle dynamics variables, hands-free phone condition, and driver-specific variables are found as fixed parameters. The model suggests that most distracted drivers (in the handheld condition) reduce their initial speeds more slowly than undistracted drivers, reflecting their delayed initial braking that may lead to abrupt braking to avoid a rear-end collision. Further, another group of distracted drivers exhibits faster braking (in the handheld condition), recognising the risk associated with mobile phone usage and delayed initial braking. Provisional licence holders are found to be slower in reducing their initial speeds than open licence holders, indicating their risk-taking behaviour because of their less experience and more sensitivity to mobile phone distraction. Overall, mobile phone distraction appears to impair the braking behaviour of young drivers, which poses significant safety concerns for traffic streams.

Exploring Crowdsourced Hard—Acceleration and Braking Event Data for Evaluating Safety Performance of Low-Volume Rural Highways in Iowa

There are over four million miles of two-lane roadways across the United States, of which a substantial portion is low-volume roads (LVR). Traditionally, most traffic safety efforts and countermeasures focus on high-volume high-crash urban locations. This is because LVRs cover an extensive area, and the rarity of crashes makes it challenging to use crash data to monitor the safety performance of LVRs regularly. In addition, obtaining up-to-date roadway information, such as pavement or shoulder conditions of an extensive LVR network, can be exceptionally difficult. In recent times, crowdsourced hard-acceleration and braking event data have become commercially available, which can provide precise geolocation information and can be readily acquired from different vendors. The present paper examines the potential use of this data to identify opportunities to monitor the safety of LVRs. This research examined approximately 12 million hard-acceleration and hard-braking events over a 3-months period and 26,743 crashes, including 9373 fatal injuries over the past 5-year period. The study found a moderate correlation between hard acceleration/hard-braking events with historical crash events. This study conducted a hot spot analysis using hard-acceleration/hard-braking and crash datasets. Hotspot analysis detected spatial clusters of high-risk crash locations and detected 848 common high-risk sites. Finally, this paper proposes a combined ranking scheme that simultaneously considers historical crash events and hard-acceleration/hard-braking events. The research concludes by suggesting that agencies can potentially use the hard-acceleration and hard-braking event dataset along with the historical crash dataset to effectively supervise the safety performance of the vast network of LVRs more frequently.

Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator

Occupants exposed to low or moderate crash events can already suffer from whiplash-associated disorders leading to severe and long-lasting symptoms. However, the underlying injury mechanisms and the role of muscle activity are not fully clear. Potential increases in injury risk of non-nominal postures, i.e., rotated head, cannot be evaluated in detail due to the lack of experimental data. Examining changes in neck muscle activity to hold and stabilize the head in a rotated position during pre-crash scenarios might provide a deeper understanding of muscle reflex contributions and injury mechanisms. In this study, the influence of two different head postures (nominal vs. rotation of the head by about 63 ± 9° to the right) on neck muscle activity and head kinematics was investigated in simulated braking experiments inside a driving simulator. The braking scenario was implemented by visualization of the virtual scene using head-mounted displays and a combined translational-rotational platform motion. Kinematics of seventeen healthy subjects was tracked using 3D motion capturing. Surface electromyography were used to quantify muscle activity of left and right sternocleidomastoideus (SCM) and trapezius (TRP) muscles. The results show clear evidence that rotated head postures affect the static as well as the dynamic behavior of muscle activity during the virtual braking event. With head turned to the right, the contralateral left muscles yielded higher base activation and delayed muscle onset times. In contrast, right muscles had much lower activations and showed no relevant changes in muscle activation between nominal and rotated head position. The observed delayed muscle onset times and increased asymmetrical muscle activation patterns in the rotated head position are assumed to affect injury mechanisms. This could explain the prevalence of rotated head postures during a crash reported by patients suffering from WAD. The results can be used for validating the active behavior of human body models in braking simulations with nominal and rotated head postures, and to gain a deeper understanding of neck injury mechanisms.

Simulation Analysis and Verification of Temperature and Stress of Wheel-Mounted Brake Disc of a High-Speed Train

During the braking process, a large amount of heat energy is generated at the friction surfaces between the brake disc and pads and rapidly dissipates into the disc volume. In this paper, a three-dimensional thermo-mechanical coupling model of high-speed wheel-mounted brake discs containing bolted joints and contact relationships is established. The direct coupling method is used to analyze the temperature and stress of the brake discs during an emergency braking event with an initial speed of 300 km/h. A full-scale bench test is also conducted to monitor the temperatures of the friction ring and bolted joints. The simulation result shows that the surface temperature of the friction ring reaches its peak value of 414 °C after 102 s of braking, which agrees well with the bench test result. The maximum alternating thermal stress occurs in the bolt hole where the maximum circumferential compressive stress is − 658 MPa and the maximum circumferential tensile stress is 134 MPa. During the braking process, the out-of-plane deformation of the middle part of the friction ring is larger than that of the edge, which increases the axial tensile load of the connecting bolt. This work provides support for the design of brake discs and connecting bolts.

PM10 prediction for brake wear of passenger car during different test driving cycles

PM10 emissions generated from the brake wear of passenger car per braking event during three test driving cycles (WLTP, LACT, and WLTP-Brake) were studied using a finite element analysis (FEA) approach in combination with the relationship among the mass emitted rate of airborne particles versus local contact pressure and sliding speed. In addition, PM10 emissions were measured per braking event during the WLTP-Brake cycle on a brake dynamometer using an electrical low-pressure impactor (ELPI+) to validate the proposed FEA approach. The simulated and experimental results for WLTP-Brake illustrated that the proposed simulation approach has the potential to predict PM10 from brake wear per braking event, with an R2 value of 0.93. The FEA results of three test driving cycles showed that there was a gradient rise in pad wear on both sides from the inner to outer radii. The simulated PM10 emission factors during the WLTP, LACT, and WLTP-Brake were 7.9 mg km−1 veh−1, 9.8 mg km−1 veh−1, and 6.4 mg km−1 veh−1, respectively. Among three test driving cycles, the ratio of PM10 to total brake wear mass per braking event was the largest for the LACT, followed by WLTP and WLTP-Brake. From a practical application perspective, reducing the frequency of high-speed braking may be an effective way to decrease the generation of PM10 emissions.

Hardware Development and Analysis of Vehicle's Driver Awareness during Braking Event

Safety is the most important factor in both manufacturing vehicles as well as when driving and riding. Passive and active system are the two main categories of safety system, thus, this project focus on the active system which where the brake system is located to avoid any collision in critical situation especially rear-end collision. The main objective of this project is to study the driver’s braking response in car-following situations. Data Acquisition System using Arduino IDE was developed, which lead to analyzing the driver’s brake response during emergency braking event. Two types of vehicles were used, which are the Yamaha 135LC motorcycle and Perodua Myvi 1.3L, which also known as Test Vehicle 1 (TV 1) and Test Vehicle 2 (TV 2) respectively. The Data Acquisition System (DAQ) system to record the brake response time of driver in TV 2 as the driver reacts to the activation of brake lights of TV 1 by using the ESP8266 (Wi-Fi controller) was developed. The DAQ installed in TV 1 was connected wirelessly to the DAQ installed in TV 2. Arduino IDE software was used as a medium to transfer data from the push-button sensor to the laptop with the assist of ESP8266 board as the microcontroller, that is connected to the push-button sensor. Two brake lights were tested, which were standard brake lights and flashing brake lights. Each brake lights were tested with three different speeds, which are 20, 30 and 40 km/h. Experiment was carried out at night, where TV 1 braked at any straight road around the campus. Generally, the results indicates that for each speed tested for both brake lights, the brake response time increases as speed increases. The overall results suggested that flashing brake lights has a significantly shorter brake response time with average of 145, 144, 173 ms for 20, 30 and 40 km/h respectively. With average percentage of 12.8% faster brake response time.

Flow and Thermal Analysis of a Racing Car Braking System

The braking system of a racing car is one of the main design challenges. The flow around and inside the wheel of an F1 car with all braking system components is analyzed in order to evaluate the heat transfer after a braking event. Very few studies have been published on this topic, mainly due to the high confidentiality level in the racing car sector. In the present work, using an actual geometry of an early 2000s F1 car, the braking system is simulated using a CFD approach. The boundary conditions for the wheel and brake system are taken from the simulation of a vehicle model with a front wing. Different heat transfer phenomena are progressively added to the model in order to understand their effects, including thermal convection only, radiation and conjugate heat transfer. Two different vehicle velocities are simulated to quantify and compare the heat removal after a braking event. The different heat transfer mechanisms have dramatic effects on the prediction of the brake cooling results, and these are quantified in order to understand the limitations of the simplified approaches. Finally, the influence of the ambient pressure at two different altitudes on the heat transfer from a braking event is studied.

Potential and Challenges for Application-Specific Friction Characteristics of Race Brake Pads

<div class="section abstract"><div class="htmlview paragraph">As a race driver hits the pedal to trigger the braking event, a dynamic load transfer takes place in the car. This is a similar kind of weight transfer experienced on the road while stopping any vehicle abruptly. Modern race cars such as FIA-regulated Grand Touring Car classes GT3 and LMGTE produce a significant aerodynamic downforce at a reasonably high efficiency level. In this type of high downforce race cars, load variations originated by aerodynamics are added onto the mass transfer. The combination of these effects provide a braking effect with this type of cars a highly transient character. At the same time, our customers are facing the challenge of strict technical regulations, usually forbidding brake control systems. In motorsport competition, car performance is of primary priority to help our customers win championships. For the pursue of maximum performance, it is essential to make use all available load on each tyre in order to stop later, by optimizing braking efficiency and supporting the car rotate around the apex. In this analysis, the potentials of dedicated friction material developments with application-specific friction characteristics for brake torque migration are introduced, and the challenges and benefits inherent on this concept discussed. The study consists of a presentation of a simulative approach and a dynamometer testing method providing 4D characterization maps as base for this concept. The analysis is complemented by a case-study following this method, which led to the development of a new generation of digressive friction materials for race application.</div></div>

Contributions of various non-linearities to the dynamic response of an automotive drum brake during typical braking: A theoretical study

The contributions of various non-linearities to an automotive drum brake’s transient response has been studied analytically. A discrete system model with friction, contact, and kinematic non-linearities is formulated such that each of the non-linearities could be linearized independently. The contact between the brake shoes and the brake drum is modeled using Hertzian contact theory, supported by kinematic relationships derived using vector geometry for a given design. Several variants of quasi-linear models are derived from the non-linear system by linearizing individual non-linearities. Transient responses of these models were obtained numerically during a typical braking event, consisting of the impacting phase and contact phase. The linearized and non-linear responses are compared based on the time-histories, spectra, and spectrograms. The self-energizing and de-energizing effects of the brake shoes on the dynamic behavior of the system were quantified. Finally, the trends from the developed non-linear model were qualitatively and quantitatively correlated with published data.

Optimization Parameters of Disc Brake

Abstract: In recent time due to high performance of electronic component the heat generation is increasing drastically. Due to this scenario heat dissipation becomes a major issue in efficiency promation and stable operation. Silicon based microchannel heat sink fabricated using semiconductor production technique plays important role in cooling devices. The effect of the thermophysical properties of working fluids on the performance of microchannel is tested or we can say investigated. For this purpose the different working fluids are selected. water, hepthane, ammonia, methanol, and ethanol. The disc brake is a device for slowing or stopping the rotation of a wheel. Repetitive braking of the vehicle leads to heat generation during each braking event. Transient Thermal and Structural Analysis of the Rotor Disc of Disk Brake is aimed at evaluating the performance of disc brake rotor of a car under severe braking conditions and there by assist in disc rotor design and analysis. Disc brake model and analysis is done using ANSYS workbench 14.5. The main purpose of this study is to analysis the thermo mechanical behavior of the dry contact of the brake disc during the braking phase. The coupled thermal-structural analysis is used to determine the deformation and the Von Mises stress established in the disc for the both solid and ventilated disc with two different materials to enhance performance of the rotor disc. A comparison between analytical and results obtained from FEM is done and all the values obtained from the analysis are less than their allowable values. Hence best suitable design, material and rotor disc is suggested based on the performance, strength and rigidity criteria. Keywords- disc break, thermal analysis, structure analysis.

Performance of Diaphragm Elastic Elements in ABS

Anti-locking brake systems (ABS) are well established in the automotive industry as a safety feature. ABS generally offers superior vehicle safety by limiting the longitudinal wheel slip in a braking event with deep slip condition. The study presents a creative solution for the elastic element included in the electromagnetic valve meant to reduce the noise and vibration associated with the use of the hand brake. At the same time, the suggested solution improves the dynamics in vehicle’s ABS regulator device by allowing its working frequency to be increased and the braking space to be implicitly reduced. Drivers would have better directional control of the vehicle equipped with ABS. The performance of this constructive solution, materialized in the creation of its own high frequency, is showcased and confirmed in this study by the results obtained experimentally.

Comparison across vehicles of passenger head kinematics in abrupt vehicle maneuvers

Objective: Studies of vehicle occupant motions in response to abrupt vehicle maneuvers have demonstrated movements that may result in changes in the level of protection for the occupant if a crash subsequently occurs. The previous studies have typically used a single vehicle. The current study assesses whether the patterns of occupant head movement are different across passenger vehicle types.Method: Data collection was conducted on a closed test track with the same driver for all trials. A passenger sedan, a minivan, and a pickup truck were equipped with inertial measurement units to quantify vehicle dynamics. Head location was tracked using Microsoft Kinect v2 sensor and a novel methodology that fits 3 D head scan data to the depth data acquired in the vehicle. Twelve men and women with a wide range of body size and age were recruited. The primary purpose of the study was obfuscated by telling the participants that the focus was on vehicle ride motion. Participants sat in the right front seat and wore the vehicle belt. The first event during the test track route was a hard brake (approximately 1 g) to a stop from 35 mph (56 kph). Within the space of approximately 5 min the participants also experienced two aggressive, right-going lane changes, a sharp right turn with simultaneous hard braking, and a second hard braking event. The vehicles were presented in random order for each participant. This paper presents comparison across vehicles of head motions in the braking and lane-change maneuvers.Results: Accelerations were similar across the vehicles for both braking and lane-change events. The means (standard deviations) of forward head-CG excursion in the first braking event were 162 (54), 112 (39), and 176 (46) mm for the minivan, passenger car, and truck, respectively. The forward head excursion in the passenger car was found to be significantly smaller than in the other two vehicles using a paired t-test (p < 0.01). Across vehicles, the mean excursion in the second braking exposure was smaller than in the first (p < 0.01). In the first lane change event, the mean (SD) inboard head excursions were 126 (51), 110 (49), and 140 (68) mm; the values were not significantly different across vehicles or in the second lane-change event. A detailed investigation did not reveal an explanation for the smaller head excursions in the passenger car.Discussion: This is the first quantitative occupant kinematics study to compare responses across vehicles. Although a significant difference was found between vehicles, the overall responses are similar to those observed in a previous study.Conclusions: The results confirm previous studies showing large variance in excursions across occupants. Further study is needed to understand the factors that affect responses across vehicles.

Performance Degradation During Sudden Hazardous Events: A Comparative Analysis of Use of a Phone and a Music Player During Driving

The present study attempts to statistically model and quantify the contribution of two sets of potential factors to accident risk: 1) real-time driving behavioral characteristics and 2) distraction conditions. The effects of phone and music player usage on reaction times and accident probabilities were modeled for two events: 1) a pedestrian crossing (started in peripheral vision) and 2) sudden braking (started in central vision) in an urban scenario. In total, 90 subjects drove for baseline and conversation conditions. In all, 70 and 78 of the 90 subjects drove for texting and music player tasks, respectively. Reaction times and accident probabilities were modeled with the help of generalized linear mixed model approach. The results showed that the reaction time increased by 42%, 113%, and 62% due to the presence of conversation, texting, and music player operations, respectively, for the pedestrian crossing event. Whereas, the increments were lower for the sudden braking event. Drivers with higher approach speed detected the events faster. The accident probability increased due to the texting tasks and by operating the player during driving. An increase in reaction time also led to higher risk for the pedestrian crossing event. For the sudden braking event, the effect of music-listening was not significant, but the accident risk increased if the driver approached the event at high speed. Overall, the results suggest that the algorithm for designing a collision warning system should consider the real-time driving characteristics based on event type and quantify the effects of distraction based on the type of distraction.

The comparison of auditory, tactile, and multimodal warnings for the effective communication of unexpected events during an automated driving scenario

In an automated car, users can fully engage in a distractor task, making it a primary task. Compared to manual driving, drivers can engage in tasks that are difficult to interrupt and of higher demand, the consequences can be a reduced perception of, and an impaired reaction to, warnings. In this study we compared three in-vehicle warnings (auditory, tactile, and auditory-tactile) which were presented during three highly attention capturing tasks (visual, auditory, and tactile) while the user was engaged in a self-driving car scenario, culminating in an emergency brake event where the warning was presented. The novel addition for this paper was that three set paced, attention capturing tasks, as well the three warnings were all designed in a pilot study to have comparable workload and noticeability. This enabled a direct comparison of human performance to be made between each of the attention capturing tasks, which are designed to occupy only one specific modality (auditory, visual or haptic), but remain similar in overall task demand. Results from the study showed reaction times to the tactile warning (for the emergency braking event) were significantly slower compared to the auditory and auditory-tactile (aka multimodal or multisensory) warning. Despite the similar reaction times between the in-vehicle auditory warning and the multimodal warning, the multimodal warning led to a reduced number of missed warnings and fewer false responses. However, the auditory and auditory-tactile warnings were rated significantly more startling than the tactile alone. Our results extend the literature regarding the performance benefits of multimodal warnings by comparing them with in-vehicle auditory warnings in an autonomous driving context. The set-pace attention capturing tasks in this study would be of interest to other researchers to evaluate the interaction in an automated driving context, particularly with hard to interrupt and attention capturing tasks.