Optimal control of vehicle dynamics for the prevention of road departure on curved roads

Run-off-Road crashes are often associated with excessive speed in curves, which may happen when a driver is distracted or fails to compensate for reduced surface friction. This work introduces an Automated Emergency Cornering (AEC) system to protect against the major effects of over-speeding on curves, especially lateral deviation leading to lane or road departure. The AEC architecture has two levels: an upper level to perform motion planning, based on the optimal control of a nonlinear particle model, and a lower level to distribute the resulting two-dimensional acceleration reference to the available actuators. The lower level adopts the recently introduced Modified Hamiltonian Algorithm (MHA), which continuously adjusts the priority between mass-centre acceleration and yaw moment demands derived from lateral stability targets. AEC makes use of a high precision map and triggers control interventions based on vehicle kinematic states and detailed road geometry. To avoid false-positive interventions, AEC is triggered only when excessive road departure is predicted for the optimal particle motion. AEC then takes control of steering and individual wheel brake actuators to perform autonomous motion control for speed and path curvature at the limits of available friction. The AEC system is tested and evaluated

Modified hamiltonian algorithm for optimal lane change with application to collision avoidance

This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. 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, which are addressed in the current paper. First, it is typically unknown whether a predefined 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: 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 use the general nonlinear optimal control formulation, include some simplifying assumptions and base optimal control on the minimization of an underlying Hamiltonian function. A particle model is used to define an initial reference in the form of a desired global mass-center acceleration vector. Beyond that, yaw moment is taken into account for the purpose of enhancing the stability of the vehicle. The Hamiltonian function is adapted as a linear function of tyre forces and can be minimized locally for individual wheels; this significantly reduces computational workload compared to the conventional approach of forcemoment allocation. Several combinations of actuators are studied to show the general applicability of the control algorithm based on a linear Hamiltonian function. The method has the potential to be used in future vehicle control systems across a wide range of safety applications and hence improve overall vehicle agility and improve safety

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance

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, due to excessive speed, or where the friction of the road drops 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 tire 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, PPR, coupled to a control algorithm, MHA, specifically designed to operate at the vehicle friction limits. This shows the MPC formulation may equally be effective for vehicle control at the friction limits. The major difference here, compared to 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 to be used in futur

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

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

Optimal control of brakes and steering for autonomous collision avoidance

This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. We separate the control problem into two phases: collision avoidance phase and lane keeping phase. The aim is to reduce the amount of overshoot after avoiding the collision. The mass-center acceleration reference is obtained from a particle motion and implemented via a novel lower-level control allocation method, the Modified Hamiltonian Algorithm (MHA). At the upper level, we compared 3 common collision avoidance strategies with the proposed control strategy and determine trade-offs between the available intervention distance vs. overshoot during the lane keeping phase. It is found that the proposed control strategy has the least overshoot. MHA is found to achieve high quality tracking of the particle acceleration and it has performance close to that of the ideal particle. Yaw moment control, an integral part of MHA, is also found to be effective in maintaining yaw stability throughout

An autonomous safety system for road departure prevention based on combined path and sideslip control

Practical limits on vehicle speed in curves depends on many factors: road friction, lateral maneuvering room, driving skill and the willingness of a driver to brakewhile cornering. This is in addition to multiple vehicle design and performance characteristics, including the classical understeer behaviour. The current paper considers an active safety control system to protect against the major effects of over-speed in curves, especially lateral drift leading to lane or road departure. The concept is to use available information about various vehicle and environmental factors-e.g. surface friction, lane and shoulder widths-and compute control interventions which will protect against lane and road departure. The proposed system is based on the optimal control of particle motions, as described previously for a Parabolic Path Reference (PPR) which provides a control reference for combined cornering and braking under computer control. The earlier work assumed very simple road geometry which is generalized in the present analysis. Further, we make use of a novel control algorithm based on nonlinear optimal control theory, capable of delivering the required integrated control of speed, path curvature and yaw stability. This paper describes the key concepts for designing and implementing such a system

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 vehicle's 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 driver's 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. © 2016 IEEE

An autonomous safety system for road departure prevention based on combined path and sideslip control

Practical limits on vehicle speed in curves depends on many factors: road friction, lateral maneuvering room, driving skill and the willingness of a driver to brakewhile cornering. This is in addition to multiple vehicle design and performance characteristics, including the classical understeer behaviour. The current paper considers an active safety control system to protect against the major effects of over-speed in curves, especially lateral drift leading to lane or road departure. The concept is to use available information about various vehicle and environmental factors-e.g. surface friction, lane and shoulder widths-and compute control interventions which will protect against lane and road departure. The proposed system is based on the optimal control of particle motions, as described previously for a Parabolic Path Reference (PPR) which provides a control reference for combined cornering and braking under computer control. The earlier work assumed very simple road geometry which is generalized in the present analysis. Further, we make use of a novel control algorithm based on nonlinear optimal control theory, capable of delivering the required integrated control of speed, path curvature and yaw stability. This paper describes the key concepts for designing and implementing such a system. © 2016 Taylor & Francis Group, London