Autonomous Control and Automotive Simulator Based Driver Training Methodologies for Vehicle Run-Off-Road and Recovery Events

Traffic fatalities and injuries continue to demand the attention of researchers and governments across the world as they remain significant factors in public health and safety. Enhanced legislature along with vehicle and roadway technology has helped to reduce the impact of traffic crashes in many scenarios. However, one specifically troublesome area of traffic safety, which persists, is run-off-road (ROR) where a vehicle\u27s wheels leave the paved portion of the roadway and begin traveling on the shoulder or side of the road. Large percentages of fatal and injury traffic crashes are attributable to ROR. One of the most critical reasons why ROR scenarios quickly evolve into serious crashes is poor driver performance. Drivers are unprepared to safely handle the situation and often execute dangerous maneuvers, such as overcorrection or sudden braking, which can lead to devastating results. Currently implemented ROR countermeasures such as roadway infrastructure modifications and vehicle safety systems have helped to mitigate some ROR events but remain limited in their approach. A complete solution must directly address the primary factor contributing to ROR crashes which is driver performance errors. Four vehicle safety control systems, based on sliding control, linear quadratic, state flow, and classical theories, were developed to autonomously recover a vehicle from ROR without driver intervention. The vehicle response was simulated for each controller under a variety of common road departure and return scenarios. The results showed that the linear quadratic and sliding control methodologies outperformed the other controllers in terms of overall stability. However, the linear quadratic controller was the only design to safely recover the vehicle in all of the simulation conditions examined. On average, it performed the recovery almost 50 percent faster and with 40 percent less lateral error than the sliding controller at the expense of higher yaw rates. The performance of the linear quadratic and sliding algorithms was investigated further to include more complex vehicle modeling, state estimation techniques, and sensor measurement noise. The two controllers were simulated amongst a variety of ROR conditions where typical driver performance was inadequate to safely operate the vehicle. The sliding controller recovered the fastest within the nominal conditions but exhibited large variability in performance amongst the more extreme ROR scenarios. Despite some small sacrifice in lateral error and yaw rate, the linear quadratic controller demonstrated a higher level of consistency and stability amongst the various conditions examined. Overall, the linear quadratic controller recovered the vehicle 25 percent faster than the sliding controller while using 70 percent less steering, which combined with its robust performance, indicates its high potential as an autonomous ROR countermeasure. The present status of autonomous vehicle control research for ROR remains premature for commercial implementation; in the meantime, another countermeasure which directly addresses driver performance is driver education and training. An automotive simulator based ROR training program was developed to instruct drivers on how to perform a safe and effective recovery from ROR. A pilot study, involving seventeen human subject participants, was conducted to evaluate the effectiveness of the training program and whether the participants\u27 ROR recovery skills increased following the training. Based on specific evaluation criteria and a developed scoring system, it was shown that drivers did learn from the training program and were able to better utilize proper recovery methods. The pilot study also revealed that drivers improved their recovery scores by an average of 78 percent. Building on the success observed in the pilot study, a second human subject study was used to validate the simulator as an effective tool for replicating the ROR experience with the additional benefit of receiving insight into driver reactions to ROR. Analysis of variance results of subjective questionnaire data and objective performance evaluation parameters showed strong correlations to ROR crash data and previous ROR study conclusions. In particular, higher vehicle velocities, curved roads, and higher friction coefficient differences between the road and shoulder all negatively impacted drivers\u27 recoveries from ROR. The only non-significant impact found was that of the roadway edge, indicating a possible limitation of the simulator system with respect to that particular environment variable. The validation study provides a foundation for further evaluation and development of a simulator based ROR recovery training program to help equip drivers with the skills to safely recognize and recover from this dangerous and often deadly scenario. Finally, building on the findings of the pilot study and validation study, a total of 75 individuals participated in a pre-post experiment to examine the effect of a training video on improving driver performance during a set of simulated ROR scenarios (e.g., on a high speed highway, a horizontal curve, and a residential rural road). In each scenario, the vehicle was unexpectedly forced into an ROR scenario for which the drivers were instructed to recover as safely as possible. The treatment group then watched a custom ROR training video while the control group viewed a placebo video. The participants then drove the same simulated ROR scenarios. The results suggest that the training video had a significant positive effect on drivers\u27 steering response on all three roadway conditions as well as improvements in vehicle stability, subjectively rated demand on the driver, and self-evaluated performance in the highway scenario. Under the highway conditions, 84 percent of the treatment group and 52 percent of the control group recovered from the ROR events. In total, the treatment group recovered from the ROR events 58 percent of the time while the control group recovered 45 percent of the time. The results of this study suggest that even a short video about recovering from ROR events can significantly influence a driver\u27s ability to recover. It is possible that additional training may have further benefits in recovering from ROR events

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