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

Abstract

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.

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…Reference [82][83] proposed a hierarchical control framework based on SMC. Yangyan Gao et al studied the vehicle yaw rate follow control method based on Hamilton algorithm [84].…”

Autonomous Wary Collision Avoidance

“…When planning vehicle maneuvers there is usually freedom to not only find a feasible path, but also to optimize the trajectory according to a criterion. In the context of critical collision avoidance, examples of such criteria are to perform minimum-time lane change [13], [15], [21], minimum-distance lane change [22], [23], minimum-distance collision avoidance [6], or collision avoidance while minimizing the overshoot in the new lane [14]. Here, considering the wary strategy to be applied by the controller in Section V, the trajectory is planned for the minimum possible friction considering collision avoidance of a single obstacle.…”

“…It is defined as the globally fixed force component perpendicular to the final velocity vector and is the force that stops the approach of the vehicle towards the obstacle [31]. Solving (14) for the passing maneuver, the force component F c,y is then computed using the vehicle longitudinal and lateral forces, F x and F y , by…”

“…The attainable forces during a maneuver were illustrated in [12] by creating plots of them at different time instants. Here, to visualize the attainable forces during one maneuver obtained by solving (14), Fig. 4 shows the attainable force volume introduced in [31], where it is seen that the force is indeed on the boundary of F c,y and M z as indicated by (16).…”

“…This concept is also used in the Modified Hamiltonian Algorithm (MHA) [13], where the trade-off between yaw-moment control and centerof-mass acceleration is determined by an iterative process, rather than through an optimization process as in [12]. The MHA controller has been applied to the problems of collision avoidance and minimum-time lane change in [14], [15]. In [16], a steering controller for lane-departure prevention was developed that exploits how optimal maneuvers tend to push the individual tires close to their friction limit, allowing explicit computation of the desired tire-slip angles from an acceleration reference.…”