Effects of Concurrent Continuous Visual Feedback on Learning the Lane Keeping Task

This study investigated the training effectiveness of continuous visual feedback in a simulator-based lane keeping task. Two groups of student drivers (total of 30 participants) were instructed to drive as accurately as possible in the center of the right lane in a self-paced driving task during five 8-min sessions. One group received visual feedback using a horizontal compensatory display positioned on the dashboard, which provided an indication of the momentary distance to the lane center during the three training sessions. During two retention sessions (immediate and one day delayed) both groups drove without the augmented feedback. The augmented feedback resulted in improved performance on a measure lane keeping accuracy, but this effect disappeared during retention. Furthermore, the augmented feedback resulted in increased steering wheel activity during all sessions, and increased driver workload in the delayed retention session. These results provide support for the guidance hypothesis and have possible implications for the use of continuous concurrent feedback in simulatorbased driver training

Unconstrained design: improving multitasking with in-vehicle information systems through enhanced situation awareness

In the age of information, in-vehicle multitasking is inevitable. The popularity of the automobile in combination with the demands of everyday life presents a demand to do more than simply focus on the road. Situation Awareness (SA) is a theory that allows designers to understand how operators interact in dynamic, complex environments. Unconstrained Design is proposed as a way of enhancing multitasking performance in-vehicle. This paper presents an experimental investigation into human-machine interface concepts that aim to support drivers to multitask in-vehicle when frequent task switching is required. Two SA-based approaches were investigated, one which focussed on supporting preparation for a Non-Driving Related Activity (NDRA), and one which focussed on supporting the Driving Related Activity (DRA) when an NDRA is active. While multitasking, Contextual Cueing, using a Head-up Display, produced significant reductions in NDRA response time while an auditory lane keeping aid increased the amount of time a driver spent in the central region of a lane. This provides evidence to suggest that using SA and Unconstrained Design as a philosophy for the design of IVIS that supports drivers’ ability to multitask in-vehicle, could lead to task performance improvements.Jaguar Land Rove

Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

ARE DISTRACTED DRIVERS AWARE THAT THEY ARE DISTRACTED?: EXPLORING AWARENESS, SELF-REGULATION, AND PERFORMANCE IN DRIVERS PERFORMING SECONDARY TASKS

Research suggests that driving while talking on a mobile telephone causes drivers not to respond to important events but has a smaller effect on their lane-keeping ability. This pattern is similar to research on night driving and suggests that problems associated with distraction may parallel those of night driving. Here, participants evaluated their driving performance before and after driving a simulated curvy road under different distraction conditions. In experiment 1 drivers failed to appreciate their distraction-induced performance decrements and did not recognize the dissociation between lane-keeping and identification. In Experiment 2 drivers did not adjust their speed to offset being distracted. Continuous feedback that steering skills are robust to distraction may prevent drivers from being aware that they are distracted

Improving Hazard Perception and Tracking Through Part-Task Training

Safe driving requires wisely allocating focal attention among multiple changing events and comprehending those events. Research suggests that attentional skills can be improved by training. This study uses a low-fidelity driving simulator to train participants using part-task training on two attentional subskills: identifying (comprehending) and tracking potential hazards; and detecting and avoiding imminent hazards. Following initial familiarization with the driving simulator, each participant received training in one of these two attentional subskills. Hazard tracking probes train (and measure) identifying and tracking potential hazards by having participants watch a moving driving scenario and then select the vehicle that behaved hazardously during the scene. In hazard avoidance probes, participants must make driving responses to avoid imminent hazards without hitting nearby vehicles. After the training phase, there is a test phase which contains hazard tracking and hazard avoidance probes. The test measures near transfer, to trained hazard types, and far transfer, to untrained hazard types. Results showed significant training effects for each skill. Participants in the hazard tracking condition performed better on hazard tracking scenarios than the hazard avoidance group, but only in near transfer. Participants who received hazard avoidance training performed better overall on hazard avoidance trials compared to those in the hazard tracking condition. Keywords: hazard tracking, hazard avoidance, attention allocation, driving simulator, hazard perception, part task trainin

Quantifying Cognitive Efficiency of Display in Human-Machine Systems

As a side effect of fast growing informational technology, information overload becomes prevalent in the operation of many human-machine systems. Overwhelming information can degrade operational performance because it imposes large mental workload on human operators. One way to address this issue is to improve the cognitive efficiency of display. A cognitively efficient display should be more informative while demanding less mental resources so that an operator can process larger displayed information using their limited working memory and achieve better performance. In order to quantitatively evaluate this display property, a Cognitive Efficiency (CE) metric is formulated as the ratio of the measures of two dimensions: display informativeness and required mental resources (each dimension can be affected by display, human, and contextual factors). The first segment of the dissertation discusses the available measurement techniques to construct the CE metric and initially validates the CE metric with basic discrete displays. The second segment demonstrates that displays showing higher cognitive efficiency improve multitask performance. This part also identifies the version of the CE metric that is the most predictive of multitask performance. The last segment of the dissertation applies the CE metric in driving scenarios to evaluate novel speedometer displays; however, it finds that the most efficient display may not better enhance concurrent tracking performance in driving. Although the findings of dissertation show several limitations, they provide valuable insight into the complicated relationship among display, human cognition, and multitask performance in human-machine systems

Old habits die hard? The fragility of eco-driving mental models and why green driving behaviour is difficult to sustain

Tangible incentives, training and feedback systems have been shown to reduce drivers’ fuel consumption in several studies. However, the effects of such tools are often short-lived or dependent on continuous cues. Several studies found that many drivers already possess eco-driving mental models, and are able to activate them, for instance when an experimenter asks them to “drive fuel-efficiently”. However, it is unclear how sustainable mental models are. The aim of the current study was to investigate the resilience of drivers’ eco-driving mental models following engagement with a workload task, implemented as a simplified version of the Twenty Questions Task (TQT). Would drivers revert to ‘everyday’ driving behaviours following exposure to heightened workload? A driving simulator experiment was conducted whereby 15 participants first performed a baseline drive, and then in a second session were prompted to drive fuel-efficiently. In each drive, the participants drove with and without completing the TQT. The results of two-way ANOVAs and Wilcoxon signed-rank tests support that they drive more slowly and keep a more stable speed when asked to eco-drive. However, it appears that drivers fell back into ‘everyday’ habits over time, and after the workload task, but these effects cannot be clearly isolated from each other. Driving and the workload task possibly invoked unrelated thoughts, causing eco-driving mental models to be deactivated. Future research is needed to explore ways to activate existing knowledge and skills and to use reminders at regular intervals, so new driver behaviours can be proceduralised and automatised and thus changed sustainably

Human-Machine Interface Development For Modifying Driver Lane Change Behavior In Manual, Automated, And Shared Control Automated Driving

Rear-end crashes are common on U.S. roads. Driver assistance and automated driving technologies can reduce rear-end crashes (among other crash types as well). Braking is assumed for forward collision warning (FCW) and automatic emergency braking (AEB) systems. Braking is also used for adaptive cruise control (ACC) and in automated driving systems more generally. However, steering may be advised in an emergency if the adjacent lane is clear and braking is unlikely to avoid a collision. Steering around an obstacle when feasible also eliminates the risk of becoming the new forward collision hazard. Driver assist technology like emergency steer assist (ESA) and Level 2 or Level 3 automated driving systems might facilitate manual emergency lane changes but may require the driver to manually initiate the maneuver, something which drivers are often reluctant to do. An Human-Machine Interface (HMI) might advise the driver of a steerable path when feasible in forward collision hazard situations. Such an HMI might also advise a driver of normal lane change opportunities that can reduce travel time, increase fuel efficiency, or simply enhance the driving experience by promoting `flow.\u27 This dissertation investigated the propensity of drivers to brake only versus steer in both manual and automated driving situations that end in a high-intensity forward collision hazard. A audio-visual Field of Safe Travel (FOST) cluster display and haptic steering wheel HMI were developed to advise drivers in both discretionary and emergency situations of a lane change opportunity. The HMI was tested in a moving base simulator in manual driving, in fully autonomous driving, and in shared-control autonomous driving during a simulated highway commute that ended in an high-intensity forward collision hazard situation. Results indicated that a) driver response was affected by the nature of the automated driving (faster response in hands-on shared control versus hands-off fully autonomous driving); b) exposure to the HMI in normal lane changes both familiarized the driver with the HMI and introduced a mental set that steering was also a possibility rather than braking only; c) but that drivers used their direct vision to determine their response in the emergency event. A methodological issue related to mental set was also uncovered and resolved through screening studies. The final study brought the dissertation full-circle, comparing hands-off fully automated driving to hands-on shared control automated driving in the context of either providing some or no exposure to the developed LCA system concept. Results of the final study indicated that shared control lies somewhere between that of manual driving and hands-off fully automate driving. Benefits were also shown to exist for the LCA system concept irrespective of whether the discrete haptic profiles are included or not. The discrete haptic profiles did not statistically reliably increase response times to the FC hazard event, although they do show a trend toward decreasing response variability. This finding solidified the fact that by implementing a system for benign driving that aids in establishing a mental set to steer around an obstacle may actually be beneficial for rear-end crash scenarios. This dissertation’s contributions include a) audio-visual FOST display concepts; b) discrete haptic steering display concepts; c) a paired-comparisons scaling for urgency for haptic displays applied while driving; d) a new ``mirage scenario\u27\u27 methodology for eliciting subjective assessments in the context of a forward collision hazard, briefly presented then removed, without risk of simulator sickness, and e) a methodological lesson for others who wish to investigate semi-automated and automated driving interventions and must manage driver mental set carefully