Stephen M. Erlien

Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

Steer-by-wire technology enables vehicle safety systems to share control with a driver through augmentation of the drivers steering commands. Advances in sensing technologies empower these systems further with real-time information about the surrounding environment. Leveraging these advancements in vehicle actuation and sensing, the authors present a shared control framework for obstacle avoidance and stability control using two safe driving envelopes. One of these envelopes is defined by the vehicle handling limits, whereas the other is defined by spatial limitations imposed by lane boundaries and obstacles. A model predictive control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while avoiding obstacles and preventing loss of control. The optimal control problem underlying the controller is inherently nonconvex but is solved as a set of convex problems allowing for reliable real-time implementation. This approach is validated on an experimental vehicle working with human drivers to negotiate obstacles in a low friction environment.

Safe Driving Envelopes for Shared Control of Ground Vehicles

Abstract Leveraging new technology in vehicle actuation and sensing, the authors present a control framework for obstacle avoidance and stability control using safe driving envelopes. One of these envelopes is defined by the vehicle handling limits and the other is based on spatial limitations imposed by the environment. A Model Predictive Control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while allowing the driver access to the full capabilities of the vehicle.

Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios

Emergency scenarios may necessitate autonomous vehicle maneuvers up to their handling limits in order to avoid collisions. In these scenarios, vehicle stabilization becomes important to ensure that the vehicle does not lose control. However, stabilization actions may conflict with those necessary for collision avoidance, potentially leading to a collision. This paper presents a new control structure that integrates path tracking, vehicle stabilization, and collision avoidance and mediates among these sometimes conflicting objectives by prioritizing collision avoidance. It can even temporarily violate vehicle stabilization criteria if needed to avoid a collision. The framework is implemented using model predictive and feedback controllers. Incorporating tire nonlinearities into the model allows the controller to use all of the vehicle’s performance capability to meet the objectives. A prediction horizon comprised of variable length time steps integrates the different time scales associated with stabilization and collision avoidance. Experimental data from an autonomous vehicle demonstrate the controller safely driving at the vehicle’s handling limits and avoiding an obstacle suddenly introduced in the middle of a turn.

Incorporating non-linear tire dynamics into a convex approach to shared steering control

Active safety systems enabled by steer-by-wire technology can share control with a driver, augmenting the drivers steering commands to avoid collisions and prevent loss of control. The extent to which this can be done is limited by the controllers ability to anticipate dangerous scenarios in order to appropriately intervene and steer the vehicle to safety. However, the non-linear nature of tire dynamics poses a challenge in predicting and modifying vehicle behavior in real-time. In this paper, online successive linearizations of the future planned vehicle trajectory approximates these non-linear dynamics in a real-time, model predictive controller that shares control with a human driver. Simulation results of aggressive maneuvers demonstrate the usefulness of this approach as well as illustrate interesting interactions between the sometimes competing objectives of vehicle stability and collision avoidance.

Predictive Haptic Feedback for Obstacle Avoidance Based on Model Predictive Control

New sensing and steering technologies enable safety systems that work with the driver to ensure a safe and collision-free vehicle trajectory using a shared control approach. These shared control systems must constantly balance the sometimes competing objectives of following the drivers command and maintaining a feasible trajectory for the vehicle. This paper presents a novel technique for creating haptic steering feedback based on a prediction of the systems need to intervene in the future. This feedback mirrors the tension between the two controller objectives of following the driver and maintaining a feasible path. The paper uses simulation and experiment to investigate the impact of varying the prediction horizon on system performance. A novel in-vehicle driver study based on decoupling visual and haptic cues demonstrates that this feedback provides a statistically significant improvement in response time and reduced time to collision (TTC) in an obstacle avoidance task.

Prioritizing collision avoidance and vehicle stabilization for autonomous vehicles

One approach to autonomous vehicle control is to generate and then track a desired trajectory without explicit consideration of vehicle stability. Stabilization is then entrusted to the vehicles built-in production systems, such as electronic stability control, which constantly augment driving inputs to ensure stability. Other approaches explicitly consider stabilization criteria and implement permanently active constraints on the vehicles actions. Situations exist, however, where enforcing stability constraints could lead to an otherwise avoidable collision. This paper presents an alternative paradigm for autonomous vehicle control that explicitly considers vehicle stability and environmental boundaries as it attempts to track a trajectory; such a mediator can choose to violate short term stability constraints in order to avoid a collision. Model predictive control provides an implementation framework, and an autonomous vehicle demonstrates the viability of the controller as it performs aggressive maneuvers. Driving around a turn at the vehicles limits exhibits the importance of vehicle stability for autonomous vehicle control. Performing an emergency double lane change, however, highlights a situation where stability criteria must be temporarily violated to avoid a collision.

Incorporating Ethical Considerations Into Automated Vehicle Control

Not only do automated vehicles need to meet specifications for technical performance, they also need to satisfy the societal expectations for behavior in traffic with humans. Societal expectations, such as accident avoidance and adherence to traffic laws, have their foundation in core moral issues found in philosophy and ethics. Thus, engineers designing control algorithms for automated vehicles can benefit from applying principles and frameworks from philosophy to drive design decisions. In particular, we use a set of ethical frameworks to map design decisions for a model predictive control problem to philosophical principles. Deontology, a rule-based ethical framework, motivates the development of constraints on the system. Consequentialism, a cost-based ethical framework, motivates the construction of the objective function. The choice of weights is guided by the concepts of virtue ethics and role morality to determine behavior for different types of vehicles. The strong link between ethical principles and actual vehicle behavior developed through this approach is demonstrated experimentally by implementing alternative design choices on a test vehicle in a simple driving scenario.

Late phasing homogeneous charge compression ignition cycle-to-cycle combustion timing control with fuel quantity input

Late-phasing homogeneous charge compression ignition (HCCI) operating conditions have the potential to expand the useful operating range of HCCI. However, these conditions exhibit significant variation in combustion timing and work output from one cycle to the next. Cyclic variations in the combustion timing of HCCI combustion at late-phasing operating conditions can be removed through the use of cycle-to-cycle control of fuel injection quantity. A nonlinear, discrete-time model of the recompression HCCI process captures the oscillations in late-phasing HCCI; when this model is linearized, it represents these dynamics as a pole on the negative real-axis. A simple lag compensator eliminates the oscillations in combustion phasing and drastically improves the operability of late-phasing HCCI in both simulation and experiment.

Multi-Cylinder HCCI Control With Cam Phaser Variable Valve Actuation Using Model Predictive Control

Recent work in the area of Homogenous Charge Compression Ignition (HCCI) engine control has focused on the use of variable valve timing (VVT) as a near term implementation strategy. Control of valve timing has a significant influence on combustion phasing and can be implemented with cam-based VVT systems already available in production vehicles. However, cam-based VVT systems pose a challenge by introducing cylinder coupling via a shared actuator. This paper presents a Model Predictive Control (MPC) framework that explicitly accounts for this inter-cylinder coupling as a constraint on the system. The prediction time step of this MPC controller differs from the execution time step, enabling consideration of shared actuation among otherwise independent systems. Experimental results on a multi-cylinder HCCI engine test bed provide validation of this controller.Copyright

The Virtual Wheel Concept for Supportive Steering Feedback During Active Steering Interventions

Active steering systems allow for improved vehicle safety and stability through steering interventions that augment a driver’s steering command. In a conventional steering system, steering feedback torque depends on the tire forces and corresponding moments that act on the roadwheels. During active steering interventions, there are differences between the driver’s command and the actual roadwheel angle. The steering feedback can now be based on either the moments acting on the actual roadwheels or the moments acting on a virtual wheel following the driver’s intended steering command. With small interventions, the difference between these two approaches is negligible. However, when the intervention is large (e.g. obstacle avoidance maneuvers), basing handwheel moments on the actual roadwheel position results in a handwheel torque that acts in opposition to the intervention. The virtual wheel concept produces a more supportive, and potentially more intuitive, handwheel torque. This reduces the discrepancy between the driver command and the active steering system in simulation and experiments.© 2014 ASME