Trajectory optimization for autonomous overtaking with visibility maximization

Conference paper (2017)

Authors

H. Andersen National University of Singapore
Wilko Schwarting Massachusetts Institute of Technology
Felix Naser Singapore-MIT Alliance
You Hong Eng Singapore-MIT Alliance
Marcelo H. Ang National University of Singapore
Daniela Rus Massachusetts Institute of Technology
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Autonomous vehicles Optimization Planning Vehicle dynamics Trajectory Roads Urban areas
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Published Date

2017

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

In this paper we present a trajectory generation method for autonomous overtaking of static obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example, the autonomous car may have to move slightly into the opposite lane in order to cleanly see in front of a car ahead. Once it has gathered enough information about the road ahead, then the autonomous car can safely overtake. We generate safe trajectories by solving, in real-time, a non-linear constrained optimization, formulated as a Receding Horizon planner. The planner is guided by a high-level state machine, which determines when the overtake maneuver should begin. Our main contribution is a method that can maximize visibility, prioritizes safety and respects the boundaries of the road while executing the maneuver. We present experimental results in simulation with data collected during real driving.

Files

08317853.pdf
(.pdf | 0.925 Mb)