Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Abstract

Abstract-High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this work, we introduce a Parallel Autonomy, or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as Nonlinear Model Predictive Control (NMPC), which includes analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for Parallel Autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles.

Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

“…where δ f is the front-wheel steering angle, r is the yaw rate based on the vehicle coordinate system, β is the sideslip angle based on the vehicle coordinate system, a x is the longitudinal acceleration based on the vehicle coordinate system, a y is the lateral acceleration based on the vehicle coordinate system, the input vector is u � δ f a x T , the state vector is x � r β u c T , and the measurement vector is y � a y . After the partial differential of equations (8) and (9) with respect to x, the Jacobian matrices F and H in Figure 3 are calculated as follows:…”

“…Kim et al [7] used an MPC path tracking control method based on quadratic programming (QP) optimization to improve the path tracking performance of autonomous vehicles. Schwarting et al [8] used a receding horizon planner based on nonlinear model predictive control (NMPC) to ensure safety collision avoidance. However, the above control methods ignore the influence of high speeds and low-adhesion roads on curved path tracking.…”

Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking With Visibility Maximization

“…For example in [20] generates and tracks trajectory in highway context. It has also been applied as parallel autonomy planner, in which the autonomous system works hand in hand with a human driver [21]. Furthermore, [22] proposed an MPC based trajectory planner for autonomous driving along the Bertha-Benz Memorial Route.…”

Highly Parallelized Data-Driven MPC for Minimal Intervention Shared Control

“…For this reason, there is a growing line of work that follows the minimum intervention principle in the semi-autonomous vehicle domain [25]. For example, Schwarting et al [28,29] describe a parallel autonomy framework that develops control trajectories for semi-autonomous vehicles that minimize deviation from userinput and achieve task-specific metrics like road following and contour tracking. Anderson et al [3,5] describe a geometric, homotopy-based algorithm for computing free space in the environment.…”