arXiv subjects

Cody Fleming

Publications and source records attributed to Cody Fleming.

40 records · Page 3Linked to original sources

arXiv2019

Learning Model Predictive Control for Connected Autonomous Vehicles

A Learning Model Predictive Controller (LMPC) is presented and tailored to platooning and Connected Autonomous Vehicles (CAVs) applications. The proposed controller builds on previous work on nonlinear LMPC, adapting its architecture and extending its capability to (a) handle dynamic environments and (b) account for data-driven decision variables that derive from an unknown or unknowable function. The paper presents the control design approach, and shows how to recursively construct an outer loop candidate trajectory and an inner iterative LMPC controller that converges to an optimal strategy over both model-driven and data-driven variables. Simulation results show the effectiveness of the proposed control logic.

math.OC↗

arXiv2019

Towards Improved Testing For Deep Learning

The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.

cs.SE↗

arXiv2018

PAIM: Platoon-based Autonomous Intersection Management

With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stop-sign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.

eess.SY↗

arXiv2017

A Systems Approach for Eliciting Mission-Centric Security Requirements

The security of cyber-physical systems is first and foremost a safety problem, yet it is typically handled as a traditional security problem, which means that solutions are based on defending against threats and are often implemented too late. This approach neglects to take into consideration the context in which the system is intended to operate, thus system safety may be compromised. This paper presents a systems-theoretic analysis approach that combines stakeholder perspectives with a modified version of Systems-Theoretic Accident Model and Process (STAMP) that allows decision-makers to strategically enhance the safety, resilience, and security of a cyber-physical system against potential threats. This methodology allows the capture of vital mission-specific information in a model, which then allows analysts to identify and mitigate vulnerabilities in the locations most critical to mission success. We present an overview of the general approach followed by a real example using an unmanned aerial vehicle conducting a reconnaissance mission.

cs.CR↗