Optimizing Maneuver Length for Autonomous Obstacle Avoidance Maneuver with Considerations for Controllability and Passenger Comfort on Low Friction Surfaces

Document Type

Article

Publication Date

2-15-2022

Department

Department of Mechanical Engineering-Engineering Mechanics; Department of Electrical and Computer Engineering

Abstract

In order for autonomous vehicles to be widely adopted, they must be able to operate in all conditions possible in the regions they are operating. In northern climates, this means they must be able to operate on low friction due to the presence of ice or snow. An autonomous vehicle performing an obstacle avoidance maneuver in the form of a double-lane change maneuver must account for low friction when present to ensure a safe and comfortable ride for passengers. This work presents a graphical optimization method for determining a minimum maneuver distance based on surface friction coefficient, which is constrained by cross-track error and lateral acceleration. The optimization has been performed in simulation and refined with hardware results from an autonomous test vehicle, showing the optimal maneuver length being dominated by a constraint on lateral acceleration on surfaces with a friction coefficient close to that of packed snow and above and by the cross-track error constraint on icy surfaces. When the friction coefficient is known precisely, a lookup table based on friction coefficient can be used to determine the maneuver length. If the friction coefficient is estimated from broad categories of surface type such as ice, snow, or pavement, the maneuver length is fixed at 120 m for icy surfaces (assuming the ice is the lowest friction encountered by the vehicle during hardware testing) and 70 m for all other surfaces. This work shows the process of optimizing maneuver length across friction surfaces for a single velocity, which would be repeated for the range of velocities a production autonomous vehicle could be assumed to operate during normal use.

Publication Title

SAE International Journal of Connected and Automated Vehicles

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