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

Abstract

In this paper we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion. Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility. The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated with the overtaking maneuver, to determine when the overtake maneuver should happen. The approach is validated in simulation and in experiments in real world traffic.

A model predictive obstacle avoidance method based on dynamic motion primitives and a Kalman filter

“…The purpose of MPC is to compute the optimal control sequence $$$ {\left[{\mathbf{u}}_k^{\ast}\right]}_{k=0}^{H-1} $$$, where $$$ H $$$ is the prediction horizon (prediction time steps). Given the initial state $$$ {\mathbf{x}}_0 $$$ of the prediction horizon, the optimal control sequence $$$ {\left[{\mathbf{u}}_k^{\ast}\right]}_{k=0}^{H-1} $$$ in this horizon can be obtained according to the receding horizon principle, which can be solved as follows [17]: $$$$ \min J\left({\left[{\boldsymbol{\eta}}_k\right]}_{k=0}^H,\kern0.5em {\left[{\mathbf{u}}_k\right]}_{k=0}^{H-1}\right), $$$$ subject to …”

“…in this horizon can be obtained according to the receding horizon principle, which can be solved as follows [17]:…”

“…A state interception method based on a convex MPC formulation was used to generate a collision‐free trajectory for a quadrotor [16]. The MPC formulation allows vehicles to make a more informed and therefore safer decisions, thus enabling autonomous vehicles to avoid dynamic obstacles [17]. These MPC‐based methods are mostly used for autonomous vehicle and unmanned aerial vehicle (UAV).…”

Visibility-aware Trajectory Optimization with Application to Aerial Tracking

“…Lacking the environmental perception, this approach requires complete knowledge of the environment, thus not applicable in real applications. Andersen et al [10] consider OE in trajectory planning to get more information for vehicle overtaking. They generate motions by maximizing the visibility ahead of obstacles with an MPC receding horizon planner.…”

IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways

“…The trajectory optimization method is formulating the motion planning as an optimization problem. Model Predictive control (MPC) has been proven well suited for formulating the problem [13]. MPC can take the updating of the environment into account during the planning process, because of its ability to handle multi-constraints [14].…”