A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)

A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years

Shared control strategies for automated vehicles

188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada

Getting back into the loop: the perceptual-motor determinants of successful transitions out of automated driving

Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to Automated Vehicle (AV)-Human ‘transitions’. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviours (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarised as a perceptual-motor loop that requires i) calibration and ii) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modelling the coordination of gaze and steering, and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-Human transitions. Future studies need to provide an understanding of human behaviour which will be sufficient to capture the essential characteristics of drivers re-engaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering, and potential routes to improving manual control recovery

Safe and seamless transfer of control authority - exploring haptic shared control during handovers

This research aimed at investigating the impact of lateral assistance systems on drivers' performance and behaviour during transitions from Highly Automated Driving (HAD). The thesis focused on non-critical transitions and analysed the differences between system and user-initiated transitions. Hence, two experiments were developed and conducted in driving simulators to address questions relating to how handover procedures, which provide varying levels of lateral assistance, affect drivers' performance and behaviour at different stages of the transition. In particular, it was investigated which type of assistance yields better results depending on who initiated the transition of control. Drivers were induced to be Out-Of-The-Loop (OOTL) during periods of HAD and then exposed to both system and user-initiated transitions. Results showed that after user-initiated transitions, drivers were generally more engaged with the steering task and the provided assistance was not helpful and, in some cases, caused steering conflicts and a comfort drop. On the contrary, after system-initiated transitions, drivers were not engaged with the steering control and were more prone to gaze wandering. Strong lateral assistance proved to be most beneficial within the first 5 seconds of the transition, when drivers were not committed to the steering control. The provision of assistance at an operational level, namely when drivers had to keep the lane centre, was not enough to ensure good performance at a tactical level. Drivers were able to cope with tactical tasks, presented as lane changes, only after around 10 seconds from the start of the transitions in both user and system initiated cases (Chapter 3 and Chapter 4). The introduction of non-continuous lateral assistance, used to trigger steering conflicts and, in turn, a faster steering engagement, did not yield particular benefits during user-initiated transitions but it might have triggered a faster re-engagement process in system-initiated ones (Chapter 5). The results suggest that assisting drivers after user-initiated transitions is not advisable as the assistance might induce steering conflicts. On the contrary, it is extremely beneficial to assist drivers during system-initiated transitions because of their low engagement with the driving task. The thesis concludes with a general overview of the conducted studies and a discussion on future studies to take this research forward

An Internal Model Principle Approach to Modeling Predictive Human Motor Behavior

For the sensorimotor system to complete motor tasks it controls the body, it controls objects that the sensorimotor system acts upon within the environment, and it anticipates future states of the environment. The sensorimotor system is known to adapt and improve in performance with practice in response to predictable phenomena. The literature explains motor adaptation and performance improvement in terms of models, called internal models, of future loads. The theory of internal models has been investigated in the neuroscience and human motor behavior communities, where electrophysiological data and motor performance experiments have yielded rich data in support of the role of predictive modeling. Internal models can be divided into two types: internal models of the plant and internal models of exogenous processes. While internal models of the plant have a rich history and have been studied extensively, literature on internal models of exogenous processes is less developed. This dissertation introduces the Internal Model Principle (IMP) as a tool for modeling internal models of exogenous processes. This dissertation further extends the usefulness of the IMP for modeling human motor control by extending the model to handle sensorimotor tasks that feature signal blanking. Haptic feedback can be considered as an exogenous signal (a disturbance) whose features can be predicted because they are produced by the plant under control. Haptic feedback is an information signal providing the receiver feedback about the state of the system. However, haptic feedback is also a power signal; sufficient force due to haptic feedback can backdrive the biomechanics of a participant. In this dissertation these topics are explored in two studies, one in the context of driving oscillations in a spring-mass system and the other in the context of shared control design for semi-autonomous vehicles.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169870/1/scutlip_1.pd

Understanding and Modeling the Human Driver

This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65021/1/MacAdam_2003 VSD Understanding and Modelling the Driver.pd

Influence of accelerator pedal force feedback on truck drivers' speed control

The UK government has set clear targets for 80% reductions (compared with 1990 levels) in greenhouse gas emissions by 2050 and pressure is increasing on the road transport industry to reduce the fuel consumption and harmful exhaust emissions of Heavy Goods Vehicles (HGVs). Vehicle manufacturers and operators alike are having to investigate and find new ways of making reductions. It is thought that improving driver behaviour offers significant potential for these reductions in fuel consumption and emissions. This thesis considers the use of Active Accelerator Pedals (AAPs) and the potential for improved driver performance that they may offer by providing pedal force feedback to the driver. In order to develop understanding of the interactions between the human driver and accelerator pedal, two near identical tractor units, operated by Turners of Soham Ltd, were fitted with the a data logger. Data was collected and stored over a period of four months as they operated on the road. This data provided the basis for a vehicle model to be developed using real-world conditions, rather than strictly controlled test track conditions. Analysis of the behaviour of the two drivers also identified differences is styles, and explained 7% fuel consumption differences between the two drivers when negotiating roundabouts. A new mathematical model of the human driver’s longitudinal control was also developed to include the driver’s cognitive control of the accelerator pedal. Model Predictive Control theory, commonly used for modelling the driver’s steering control, was used and different driving styles were replicated by varying the weightings in a cost function, and a series of driving simulator experiments were performed to validate the model. Nine human drivers, two of which were professionals, performed two driving scenarios (drive cycle and car-following). The driver model was fitted to each driver individually to mathematically express the differences in their styles. The simulated RMS pedal forces from the fitted driver models lay within 20% of the measured simulator values. The driver model was also extended to include the interactions between a human driver and an AAP using mathematical game theory. Three frameworks were proposed: decentralised, cooperative and one-sided cooperative, but, as the cooperative framework would have been very difficult to implement experimentally, it was only considered theoretically. The same nine human drivers were presented with drive cycle and car-following scenarios whilst being assisted by pedal feedback to validate the model. Both decentralised and one-sided cooperative frameworks were applied to the fitting and compared. In the drive cycle scenario, the one-sided cooperative framework output an identical controller to the decentralised framework. In the car-following scenario, the one-sided cooperative framework produced the best fit, suggesting that the human drivers adapted their strategy to reflect the guidance from the AAP. It was noted in both scenarios that the peak pedal displacement decreased by approximately 20% with the presence of pedal force feedback. Further work is suggested to improve the mass and road gradient data obtained from the data loggers in vehicles in order to reduce the uncertainty in the traction force and fuel rate maps. With a model for the interactions of a human driver with an AAP now in place, the pedal feedback strategy can now be optimised to improve the performance of the human driver.Centre for Sustainable Road Freight and EPSR

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system