Computation of Autonomous Safety Maneuvers Using Segmentation and Optimization

Abstract

This is a Swedish Licentiate Thesis. Swedish postgraduate education leads to a Doctoral degree and/or a Licentiate degree. A Doctoral degree comprises 240 ECTS credits (4 years of full-time studies). A Licentiate degree comprises 120 ECTS credits, of which at least 60 ECTS credits constitute a Licentiate thesis.

Analysis and design of recovery behaviour of autonomous-vehicle avoidance manoeuvres

“…For the insights valuable in the analysis of the full Jacobian (41), only the case k α,r = 0 is needed. A more complete analysis of both factors can be found in [22]. The factor k α,r in (51) is zero in the cases when F x,r = 0 (implying being on the top or bottom of the friction ellipse and k α,r is, therefore, zero) or when driving straightforward with no lateral slip (F x,r arbitrary, but α r = 0 and δ = 0).…”

Lane-deviation penalty formulation and analysis for autonomous vehicle avoidance maneuvers

“…Solutions to the considered problem using the LDP objective function are compared with solutions obtained using other formulations: a squared lateral-error norm, the Huber loss criterion, and a minimum-time objective function. The alternative formulations are presented with the recovery-behavior extension 14 for improved performance in the recovery phase. Then, the obtained maneuvers for different formulations are compared with each other in terms of time spent in the opposing lane, maximum acceleration for the maneuver, and behavior differences.…”

“…The motion-planning problem for the double lane-change maneuver is represented as an optimal control problem. The lane-deviation penalty (LDP) function, 13 associating a cost for driving in the opposing lane, and a recovery-behavior extension, 14 promoting smooth return to the original lane, are adopted in the criterion. The particular choice of the parameters for the road configuration is defined in this section, and the resulting maneuver for the nominal parameters is subsequently presented.…”

“…It aims for a smooth and swift motion in the recovery phase after the obstacle, and it includes five terms that have the following purposes: to complement the LDP function (34) such that the vehicle is promoted to drive in the middle of its own driving lane after the obstacle (see the solid thick line in Figure 6(b) for a projection of this combination); to promote velocity recovery using a small penalty on the velocity deviation; to promote straight driving by including a small time penalty, which from many possible constant-velocity paths favors the straight path; two terms to include regularization of inputs to address actuator-redundancy singularities. 14 The sum of the terms multiplied with H X 1 , which makes these terms to be active only after the obstacle, defines the recovery-behavior extension 14…”

Autonomous Vehicle Maneuvering at the Limit of Friction

“…In analogy with how the vehicle behavior under mild driving conditions can be explained by simplified models, it is of interest to find control principles that support the development of future active-safety systems capable of operating at the limit of friction. Optimal control has many applications related to vehicle dynamics [20][21][22][23], and can in this case be used to find control principles that capture the essential behavior of optimal vehicle maneuvering in safety-critical situations. Such control principles can lead to simplifications in the problems of planning, control, and parameter estimation for autonomous vehicle maneuvering at the limit of friction.…”