Why Driving Performance Measures Are Sometimes Not Accurate (and Methods to Check Accuracy)

This paper identifies common sources of inconsistency and error in measurements of driving performance and describes methods to determine the size of these errors. Major sources of inconsistency and error discussed include (1) the lack of zeroing procedures (which affects measurements of steering wheel angle), (2) unknown input and output mapping (which affects measurements of throttle position), (3) the failure to control critical factors such as tire pressure, traffic, and wind (which affects measurements of speed), (4) uncertainty about where the lane boundary actually is (which affect measurements of lane position and counts of lane departures), and (5) the failure to define or use consistent definitions for measures such as headway/gap, time to line crossing, and time to collision. The lack of or inconsistency of definitions can lead to multiple interpretations of what could have been measured, and differences between interpretations are of practical significance. The types and magnitude of these inconsistencies and errors vary with the measurement platform, complicating the comparison of driving studies and interfering with building a body of knowledge of driving. By making the driving research community aware of these problems, they can be identified, assessed, and minimized in the future, and published research can be read with a more critical eye

Understanding the Automotive Pedal Usage and Foot Movement Characteristics of Older Drivers

The purpose of this study was to understand the pedal usage characteristics of older drivers in various driving tasks using an instrumented vehicle. This study stemmed from the prevalence of the pedal application errors (PAEs) and the older drivers’overrepresentation in crashes caused by PAEs. With the population increasing and becoming older, it is estimated that in 2020 there will be 40 million drivers over the age of 65 in the United States. Compared with their younger counterparts, older drivers are facing declining cognitive and physical abilities, such as impaired vision, slower reaction time and diminishing range of limb motion. Because these abilities are closely associated both with the driving task and the ability to recover from a crash, older drivers are overrepresented in vehicle crash involvement rate, and they are especially vulnerable to injuries caused by the crashes. Pedal misapplication crash is a type of crash preceded by a driver mistakenly pressing the accelerator pedal. Recently, the National Highway Traffic Safety Administration issued a report on PAE. The report reveals that older drivers are overrepresented in pedal misapplication crashes and that several driving tasks are overrepresented, such as emergency stopping, parking lot maneuvers and reaching out of the vehicle to interact with a curb-side device such as a card reader, mailbox, or ATM. Existing research has investigated the PAEs from different perspectives, but questions remain as to why older drivers are more likely to commit PAEs in these driving tasks. The current study investigated the pedal usage characteristics of 26 older drivers in driving tasks, such as startle-braking, forward parking and reaching out from the vehicle, which are scenarios associated with higher risk of PAEs. Ten stopping tasks were also investigated as baseline tasks. The study was conducted on-road using an instrumented vehicle. The data collected by the instrumented vehicle included pedal travel (potentiometer), force applied on the pedals (Tekscan sensor), and video recordings of each driver’s upper body and his or her foot movement. The study findings include the following: a) There are significantly positive correlations between a driver’s stature and the percent of foot pivoting, as well as between the shoe length and the percent of foot pivoting, which means the taller the driver or the longer the driver’s shoe, the more likely the driver will use foot pivoting instead of foot lifting in the baseline stopping tasks; b) In the startle-braking task, the driver is more likely to use foot lifting than that in the baseline tasks; c) The foot movement strategy is not found to affect lateral foot placement in either the baseline stopping tasks or the startle-braking task; d) When reaching out of the driver’s window to swipe a card at a card reader, the lateral foot placement on the brake pedal will bias rightward, compared with the lateral foot placement prior to reaching out; e) Approaching a gated access or parking in a dark, relatively confined parking space does not significantly slow down a driver’ foot transfer from the accelerator pedal to the brake pedal; f) Stature of a driver does not significantly affect the time required to successfully complete a card-swiping task. A driver’s pedal operation characteristics are associated with many factors, among which four factors are identified to be relevant to the driver’s pedal operation: stature, shoe length, startle stimuli and reaching out of the driver’s window. To identify the direct causes of PAEs, future research should investigate the pedal operation characteristics in a more controlled environment. For example, an eye-tracking device can be used to study the relationship between gaze direction and foot movement. Other driving scenarios, such as reversing, should be studied as well. In addition, a study with a larger sample size and novice drivers is necessary to validate the findings of the current study and to understand the PAEs among the population with little driving experience. The current study has both clinical and engineering implications. For occupational therapists and driving rehabilitation specialists, factors such as stature, leg length, footwear, vehicle type and pedal configuration may provide information about driver’s foot behaviors. For example, drivers with flat-soled shoes may tend to use foot lifting and drivers with wedged shoes may tend to use foot pivoting. Drivers with very wide shoes may get the shoe caught under the brake pedal when pivoting from the accelerator pedal to the brake pedal. Drivers with short leg length may be able to use foot pivoting when driving a sports vehicle, but they would have to use foot lifting when driving a large truck. Drivers tend to use foot lifting when the pedals are higher above from the vehicle floor and drivers tend to use foot pivoting when the pedals are lower above the vehicle floor. An in-clinic test of a driver’s lower extremity functions prior to on-road assessment helps to select the appropriate test vehicles. For example, it is recommended that shorter drivers with weaker lower extremity functions use vehicles of which the pedals are lower above the vehicle floor. To reduce the chance of a driver’s foot slipping off the brake pedal, engineers should consider redesigning the pedal pad to increase the friction coefficient of shoe-pedal contact. For example, using tread width of 2mm produces higher friction values. In addition, Automatic Vehicle Identification can be implemented so that the drivers do not have to reach out of the window to swipe card and to enter a gated access. Other driver assistance systems such as Autonomous Emergency Braking and Automated Parking System can either mitigate the damage or eliminate the chance of a human error

A Detection and Mitigation System for Unintended Acceleration: An Integrated Hybrid Data-driven and Model-based Approach

This study presents an integrated hybrid data-driven and model-based approach to detecting abnormal driving conditions. Vehicle data (e.g., velocity and gas pedal position) and traffic data (e.g., positions and velocities of cars nearby) are proposed for use in the detection process. In this study, the abnormal driving condition mainly refers to unintended acceleration (UA), which is the unintended, unexpected, uncontrolled acceleration of a vehicle. It is often accompanied by an apparent loss of braking effectiveness. UA has become one of the most complained-about vehicle problems in recent history. The data-driven algorithm aims to use historical data to develop a model that describes the boundary between normal and abnormal vehicle behavior in the vehicle data space. At first, several detection models were created by analyzing historical vehicle data at specific moments such as acceleration peaks and gear shifting. After that, these models were incorporated into a detection system. The system decided if a UA event had occurred by sending real-time vehicle data to the models and comprehensively analyzing their diagnostic results. Besides the data-driven algorithm, a driver model-based approach is proposed. An adaptive and rational driver model based on game theory was developed for a human driver. It was combined with a vehicle model to predict future vehicle behavior. The differences between real driving behavior and predicted driving behavior were recorded and analyzed by the detection system. An unusually large difference indicated a high probability of an abnormal event. Both the data-driven approach and the model-based approach were tested in the Simulink/dSPACE environment. It allowed a human driver to use analog steering wheels and pedals to control a virtual vehicle in real time and made tests more realistic. Vehicle models and traffic models were created in dSPACE to study the influences of UA and ineffective brakes in various roadway driving situations. Test results show that the integrated system was capable of detecting UA in one second with high accuracy. Finally, a brake assist system was designed to cooperate with the detection system, which reduced the risk of accidents

PNEUMATICALLY ACTUATED ACTIVE SUSPENSION SYSTEM FOR REDUCING VEHICLE DIVE AND SQUAT

This manuscript provides a detailed derivation of a full vehicle model, which may be used to simulate the behavior of a vehicle in longitudinal direction. The dynamics of a 14 degrees of freedom (14- DOF) vehicle model is derived and integrated with an analytical tire dynamics namely Calspan tire model. The full vehicle model is then validated experimentally with an instrumented experimental vehicle based on the driver input from brake or throttle. Several transient handling tests are performed, including sudden acceleration test and sudden braking test at constant speed. Comparisons of the experimental result and model response with sudden braking and throttling imposed motion are made. The results of model validation showed that the trends between simulation results and experimental data are almost similar with acceptable error. An active suspension control system is developed on the validated full vehicle model to reduce unwanted vehicle motions during braking and throttling maneuver. A proportional-integral-derivative (PID) scheme integrated with pitch moment rejection loop is proposed to control the system. In presented scheme the result verify improved performance of the proposed control structure during braking and throttling maneuvers compared to the passive vehicle system. It can also be noted that the additional pitch moment rejection loop is able to further improve the performance of the PID controller for the system. The proposed controller will be used to investigate the benefits of a pneumatically actuated active suspension system for reducing unwanted vehicle motion in longitudinal direction

Ergonomics of intelligent vehicle braking systems

The present thesis examines the quantitative characteristics of driver braking and pedal operation and discusses the implications for the design of braking support systems for vehicles. After the current status of the relevant research is presented through a literature review, three different methods are employed to examine driver braking microscopically, supplemented by a fourth method challenging the potential to apply the results in an adaptive brake assist system. First, thirty drivers drove an instrumented vehicle for a day each. Pedal inputs were constantly monitored through force, position sensors and a video camera. Results suggested a range of normal braking inputs in terms of brake-pedal force, initial brake-pedal displacement and throttle-release (throttle-off) rate. The inter-personal and intra-personal variability on the main variables was also prominent. [Continues.

Experimental Security Analysis of DNN-based Adaptive Cruise Control under Context-Aware Perception Attacks

Adaptive Cruise Control (ACC) is a widely used driver assistance feature for maintaining desired speed and safe distance to the leading vehicles. This paper evaluates the security of the deep neural network (DNN) based ACC systems under stealthy perception attacks that strategically inject perturbations into camera data to cause forward collisions. We present a combined knowledge-and-data-driven approach to design a context-aware strategy for the selection of the most critical times for triggering the attacks and a novel optimization-based method for the adaptive generation of image perturbations at run-time. We evaluate the effectiveness of the proposed attack using an actual driving dataset and a realistic simulation platform with the control software from a production ACC system and a physical-world driving simulator while considering interventions by the driver and safety features such as Automatic Emergency Braking (AEB) and Forward Collision Warning (FCW). Experimental results show that the proposed attack achieves 142.9x higher success rate in causing accidents than random attacks and is mitigated 89.6% less by the safety features while being stealthy and robust to real-world factors and dynamic changes in the environment. This study provides insights into the role of human operators and basic safety interventions in preventing attacks.Comment: 18 pages, 14 figures, 8 table

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles - Technological and Methodical Approaches

Fahrerassistenzsysteme sowie automatisiertes Fahren leisten einen wesentlichen Beitrag zur Verbesserung der Verkehrssicherheit von Kraftfahrzeugen, insbesondere von Nutzfahrzeugen. Mit der Weiterentwicklung des automatisierten Fahrens steigt hierbei die funktionale Leistungsfähigkeit, woraus Anforderungen an neue, gesamtheitliche Erprobungskonzepte entstehen. Um die Absicherung höherer Stufen von automatisierten Fahrfunktionen zu garantieren, sind neuartige Verifikations- und Validierungsmethoden erforderlich. Ziel dieser Arbeit ist es, durch die Aggregation von Testergebnissen aus wissensbasierten und datengetriebenen Testplattformen den Übergang von einer quantitativen Kilometerzahl zu einer qualitativen Testabdeckung zu ermöglichen. Die adaptive Testabdeckung zielt somit auf einen Kompromiss zwischen Effizienz- und Effektivitätskriterien für die Absicherung von automatisierten Fahrfunktionen in der Produktentstehung von Nutzfahrzeugen ab. Diese Arbeit umfasst die Konzeption und Implementierung eines modularen Frameworks zur kundenorientierten Absicherung automatisierter Fahrfunktionen mit vertretbarem Aufwand. Ausgehend vom Konfliktmanagement für die Anforderungen der Teststrategie werden hochautomatisierte Testansätze entwickelt. Dementsprechend wird jeder Testansatz mit seinen jeweiligen Testzielen integriert, um die Basis eines kontextgesteuerten Testkonzepts zu realisieren. Die wesentlichen Beiträge dieser Arbeit befassen sich mit vier Schwerpunkten: * Zunächst wird ein Co-Simulationsansatz präsentiert, mit dem sich die Sensoreingänge in einem Hardware-in-the-Loop-Prüfstand mithilfe synthetischer Fahrszenarien simulieren und/ oder stimulieren lassen. Der vorgestellte Aufbau bietet einen phänomenologischen Modellierungsansatz, um einen Kompromiss zwischen der Modellgranularität und dem Rechenaufwand der Echtzeitsimulation zu erreichen. Diese Methode wird für eine modulare Integration von Simulationskomponenten, wie Verkehrssimulation und Fahrdynamik, verwendet, um relevante Phänomene in kritischen Fahrszenarien zu modellieren. * Danach wird ein Messtechnik- und Datenanalysekonzept für die weltweite Absicherung von automatisierten Fahrfunktionen vorgestellt, welches eine Skalierbarkeit zur Aufzeichnung von Fahrzeugsensor- und/ oder Umfeldsensordaten von spezifischen Fahrereignissen einerseits und permanenten Daten zur statistischen Absicherung und Softwareentwicklung andererseits erlaubt. Messdaten aus länderspezifischen Feldversuchen werden aufgezeichnet und zentral in einer Cloud-Datenbank gespeichert. * Anschließend wird ein ontologiebasierter Ansatz zur Integration einer komplementären Wissensquelle aus Feldbeobachtungen in ein Wissensmanagementsystem beschrieben. Die Gruppierung von Aufzeichnungen wird mittels einer ereignisbasierten Zeitreihenanalyse mit hierarchischer Clusterbildung und normalisierter Kreuzkorrelation realisiert. Aus dem extrahierten Cluster und seinem Parameterraum lassen sich die Eintrittswahrscheinlichkeit jedes logischen Szenarios und die Wahrscheinlichkeitsverteilungen der zugehörigen Parameter ableiten. Durch die Korrelationsanalyse von synthetischen und naturalistischen Fahrszenarien wird die anforderungsbasierte Testabdeckung adaptiv und systematisch durch ausführbare Szenario-Spezifikationen erweitert. * Schließlich wird eine prospektive Risikobewertung als invertiertes Konfidenzniveau der messbaren Sicherheit mithilfe von Sensitivitäts- und Zuverlässigkeitsanalysen durchgeführt. Der Versagensbereich kann im Parameterraum identifiziert werden, um die Versagenswahrscheinlichkeit für jedes extrahierte logische Szenario durch verschiedene Stichprobenverfahren, wie beispielsweise die Monte-Carlo-Simulation und Adaptive-Importance-Sampling, vorherzusagen. Dabei führt die geschätzte Wahrscheinlichkeit einer Sicherheitsverletzung für jedes gruppierte logische Szenario zu einer messbaren Sicherheitsvorhersage. Das vorgestellte Framework erlaubt es, die Lücke zwischen wissensbasierten und datengetriebenen Testplattformen zu schließen, um die Wissensbasis für die Abdeckung der Operational Design Domains konsequent zu erweitern. Zusammenfassend zeigen die Ergebnisse den Nutzen und die Herausforderungen des entwickelten Frameworks für messbare Sicherheit durch ein Vertrauensmaß der Risikobewertung. Dies ermöglicht eine kosteneffiziente Erweiterung der Validität der Testdomäne im gesamten Softwareentwicklungsprozess, um die erforderlichen Testabbruchkriterien zu erreichen