Yangyan Gao

A flexible hierarchical control method for optimal collision avoidance

Modern active safety systems on road vehicles are capable of sophisticated motion control, e.g. for emergency braking, collision avoidance etc. - assisting or potentially overriding the driver to make speed and/or path corrections. The availability of multiple actuators - especially individual wheel braking, active front steering - enables an agile response from the vehicle, even compared to that of the most skilled human driver. For collision avoidance, a typical control approach is to: (a) define a reference geometric path that avoids collision; (b) apply low level control to perform path following. However there are a number of limitations in this approach, addressed in the current paper. First, it is typically unknown whether the reference path is feasible or over-conservative. Secondly, the control scheme is not well suited to avoiding a moving object, e.g. another vehicle. Further, any incorrect choice of reference path may degrade performance, fast adaptation to friction change is not easy to implement and the associated low-level control allocation may be computationally intensive. In this paper we make use of a particle model for initial path planning and guidance, coupled with a simplified optimal controller, used for control integration and low-level actuation. The particle trajectory is only used as a starting point for control integration; the trajectory is not required to be followed. Instead, motion is maximized in a preferred direction away from possible collision, so the particle trajectory is used for prioritization rather than strict guidance. The aim of the present paper is to show the general feasibility of a simple control algorithm based on a linear Hamiltonian function.

Vehicle optimal road departure prevention via model predictive control

This article addresses the problem of road departure prevention using integrated brake control. The scenario considered is when a high-speed vehicle leaves the highway on a curve and enters the shoulder or another lane, owing to excessive speed or a reduction in the friction of the road due to adverse weather conditions. In such a scenario, the vehicle speed is too high for the available tyre–road friction and road departure is inevitable; however, its effect can be minimized with an optimal braking strategy. To achieve online implementation, the task is formulated as a receding horizon optimization problem and solved in a linear model predictive control (MPC) framework. In this formulation, a nonlinear tyre model is adopted in order to work properly at the friction limits. The optimization results are close to those obtained previously using a particle model optimization, parabolic path reference (PPR), coupled to a control algorithm, the modified Hamiltonian algorithm (MHA), specifically designed to operate at the vehicle friction limits. This shows that the MPC formulation may be equally effective for vehicle control at the friction limits. The major difference here, compared with the earlier PPR/MHA control formulation, is that the proposed MPC strategy directly generates an optimal brake sequence, while PPR provides an optimal reference first, then MHA responds to the reference to give closed-loop actuator control. The presented MPC approach has the potential for use in future vehicle systems as part of the overall active safety control to improve overall vehicle agility and safety.

A flexible control allocation method for terminal understeer mitigation

This paper addresses the problem of terminal understeer of a road vehicle. The scenario is considered when a vehicle enters a curve with excessive speed and the aim is to apply automatic chassis control to prevent the vehicle from drifting out of the lane. In a previous study, the optimization problem is formulated as the minimization of maximum path off-tracking and the optimal response of a particle model is in the form of a parabolic path recovery (PPR) where the acceleration vector is fixed in the global frame. A recently developed model based control method the Modified Hamiltonian Algorithm (MHA) uses this acceleration information as a reference for control allocation to each wheel. The controller is developed using a simplified 3DOF vehicle model in Matlab and Simulink environment. In this paper, we consider using a high fidelity model in CarMaker to verify the control performance. It is of particular interest to see how well the chassis control can deal with the inherent understeer and oversteer qualities of the vehicle. Hence in this paper we evaluate the ability of an active safety system to overcome the mechanical limitations of the vehicle.

A novel control mediation approach to active rollover prevention

This paper considers a novel approach to active rollover prevention, incorporating online estimation of the driver’s intended path. The problem is formulated as one of minimizing lateral deviation from the intended path subject to friction and rollover limits. A recently published control allocation and moderation method is developed to be applicable to the rollover problem in such a way that speed is optimally reduced. Furthermore, a new Time-To-Rollover (TTR) metric, based on driving intention prediction, is incorporated into the system concept. Finally, mediation provides a general approach to designing a cooperative system, coordinating between the human driver and the onboard autonomous safety system to reduce the sensitivity of vehicle response to any sudden or emergency driver actions.This work evaluates the intrinsic contribution of the yaw rate reference to the overall handling performance of an electric vehicle with torque vectoring control in terms of minimum-time manoeuvring. A range of yaw rate references are compared through optimal control simulations incorporating closed-loop controller dynamics. Results show yaw rate reference has a significant effect on manoeuvre time.

Intelligent electronic steering program based on road departure mitigation control

This paper presents an intelligent electronic steering program (IESP), which combines steering shared control with electronic road departure mitigation control via individual wheel braking. It is based on a recently published control allocation and moderation method designed to improve the vehicles cornering performance in friction-limiting conditions. Here we develop the concept further in terms of driver-vehicle cooperative control; the potential benefits of electronic power assistance steering system (EPAS) are modified to guide the drivers steering behavior. A number of experiments are conducted with different drivers, using a driving simulator. The results show how the proposed IESP provides a positive control influence. The work presents a new approach to vehicle active safety involving driver-vehicle interaction control for partially automated vehicles.

Test and validation of novel Lane-Departure Prevention System

The active safety of road vehicles includes the use of electronic controls for autonomous emergency braking and avoiding unstable yaw motion. This paper focuses on a different critical situation when the vehicles overspeeds on a curve road. In this paper, a novel approach to road departure prevention is derived from a recently published control method: parabolic path reference (PPR). Previous work has compared the PPR algorithm with a more conventional electronic stability control (ESC) algorithm implemented in the high-fidelity simulation software CarMaker to validate the performance of PPR. Based on the same control theory, in this paper we take consideration of vehicle entry speed into the known curve and then simulate the vehicle performance during the whole control period in different conditions. The experimental results presented demonstrate that the vehicle can achieve improved stability and velocity-keeping performance across a range of different drivers.