Off-campus Michigan Tech users: To download campus access theses or dissertations, please use the following button to log in with your Michigan Tech ID and password: log in to proxy server

Non-Michigan Tech users: Please talk to your librarian about requesting this thesis or dissertation through interlibrary loan.

Date of Award

2021

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Darrell Robinette

Advisor 2

Jeremy Bos

Committee Member 1

James DeClerck

Committee Member 2

John Beard

Committee Member 3

Anthony Pinar

Abstract

Full market penetration for autonomous vehicle requires complete solutions for operation during winter driving conditions. This work addresses three key issues relevant to the dynamic response of an autonomous vehicle when faced with reduced friction due to snow and ice on the road when attempting a double lane change obstacle avoidance maneuver. Two low friction scenarios as well as an improvement to simulation methods are presented.

The first low friction scenario an autonomous vehicle may encounter is one in which the road surface friction coefficient is incorrectly assumed to be dry pavement. This scenario could occur in the presence of clear ice on the road which is undetectable by the vehicle until it begins traversing the effected area. In this case, the vehicle must react in a way which maintains vehicle control during the maneuver by adapting to the loss of tractive force at the wheels. This work presents a method for altering the look ahead distance of the common pure pursuit lateral control method for autonomous vehicles. This method stabilizes the vehicle during the maneuvers by dynamically changing the look ahead distance based on cross track error in addition to vehicle velocity. Implementation in the autonomous test vehicle used in this work shows an elimination of off-road occurrences during double lane changes on ice and a 46\% reduction of off-read occurrences during single lane changes.

The second low friction scenario an autonomous vehicle may encounter is one in which the road surface friction coefficient is known by the autonomous vehicle through it's own perception or through vehicle to vehicle/infrastructure communication. In this case the vehicle must plan it's path accordingly to ensure the vehicle successfully avoids the obstacle while maintaining control and passenger comfort. This work presents an optimization method which results in a minimum maneuver length across a profile of friction surfaces at a single velocity. This work also investigates the lack of correlation between the autonomous test platform operating on an icy surface and a simulation using a constant coefficient for low friction surfaces. The simulation environment used accurately predicts vehicle dynamic response when simulating operation on dry pavement with a divergence in response on friction values below that of packed snow ($\mu=0.3$). On lower friction surfaces the test vehicle exhibits significant variation in response to steering input. This work presents a stochastic method for representing friction surface in simulation across a grid map to bring simulation vehicle position prediction in line with test vehicle behavior on icy surfaces. This method shows a strong correlation between the simulation and test vehicle during rapid double lane changes and is further validation through the application of previously developed control and path planning methods.

Download

Share

COinS