About

Sean Brennan is a Professor in the Department of Mechanical Engineering at Penn State University, with affiliations including the Larson Transportation Institute. His research areas encompass Mechanical Sciences, Sensors & Controls, and Transportation Systems. Brennan's interests include ground vehicle dynamics and automation, mechatronics and embedded systems, and data representations for map-based localization and robot guidance. His work involves developing advanced control systems and sensor technologies to improve transportation safety, automation, and vehicle dynamics. Brennan has contributed to the understanding of vehicle localization, vehicle impact modeling, and the integration of sensor data for autonomous systems, demonstrating a strong focus on applying mechanical and control engineering principles to transportation and robotic systems.

Research topics

• Artificial Intelligence
• Computer Science
• Geography
• Computer Security
• Engineering
• Real-time computing
• Automotive engineering
• Control engineering
• Computer vision
• Mathematics
• Geodesy
• Telecommunications
• Electrical engineering
• Simulation

Selected publications

• Mixed-Integer MPC-Based Motion Planning Using Hybrid Zonotopes With Tight Relaxations

  IEEE Transactions on Control Systems Technology · 2026-02-18 · 2 citations

  preprintOpen access

  Autonomous vehicle (AV) motion planning problems often involve nonconvex constraints, which present a major barrier to applying model predictive control (MPC) in real time on embedded hardware. This article presents an approach for efficiently solving mixed-integer MPC motion planning problems using a hybrid zonotope representation of the obstacle-free space. The MPC optimization problem is formulated as a multistage mixed-integer quadratic program (MIQP) using a hybrid zonotope representation of the nonconvex constraints. Risk-aware planning is supported by assigning costs to different regions of the obstacle-free space within the MPC cost function. A multistage MIQP solver is presented that exploits the structure of the hybrid zonotope constraints. For some hybrid zonotope representations, it is shown that the convex relaxation is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tight</i>, i.e., equal to the convex hull. In conjunction with logical constraints derived from the AV motion planning context, this property is leveraged to generate tight quadratic program (QP) subproblems within a branch-and-bound mixed-integer solver. Simulation and processor-in-the-loop (PIL) studies are presented for obstacle-avoidance and risk-aware motion planning problems using polytopic maps and occupancy grids. In most cases, the proposed solver finds the optimal solution an order of magnitude faster than a state-of-the-art commercial solver. PIL studies demonstrate the utility of the solver for real-time implementations on embedded hardware.

  PublisherOA PDFDOI

• Performance Analysis and Optimization of Finite Impulse Response Filters Using Allan Variance

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  The design of filters seeks a separation of noise from a desired signal, and the boundary between both is a tradeoff that is a fundamental topic in signal theory. In the presence of signals wherein noise properties have time-varying components, this tradeoff is particularly challenging to optimize. The Mean Squared Error (MSE) is a standard performance metric for evaluating the performance of filters. For signals with non-white noise characteristics - which encompass nearly all real-world signals - the calculation of MSE typically requires repeated analysis across multiple experiments. Prior work by the authors introduced and extended Allan VARiance (AVAR) methods, which analyze variances within increasing data windows, to optimize Moving Average (MA) filters. That work suggested an equivalence between the time-consuming iterative process of using the MSE for filter optimization versus an analysis of the area under an AVAR curve, which can be calculated in one step. This paper extends the use of the AVAR area method for selecting an optimal Finite Impulse Response (FIR) filter, where optimality is defined as the filter that minimizes the MSE between desired and filtered signals. Prior results are further extended to illustrate that the discrete integration of the AVAR curve yields a performance index that, in one step, generates the MSE-optimal filter for input with drift (random walk) corrupted by white noise. AVAR is compared against the MSE to show that both the performance indices give nearly equivalent optimal FIR filter designs. This AVAR FIR filter optimization is achieved with only one iteration versus hundreds of iterations to optimize filters using MSE calculations.

  PublisherDOI

• Evaluating C-V2X Performance and Coverage in Work Zones

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  Cellular-vehicle-to-everything (C-V2X) technology is a promising solution for enhancing vehicular communication and augmenting road safety. Its efficacy depends greatly on its deployment within the built infrastructure and the environmental and operational conditions in the traffic network. This study uses experimental data collected from a test track and road sites to investigate the performance and coverage of C-V2X in work zones. The findings indicate that C-V2X needs an unobstructed line of sight between the vehicle’s onboard unit (OBU) and the roadside unit (RSU) for maximum functionality. The results show substantial coverage deterioration due to the presence of signal-blocking elements including structures, elevation changes, work zone vehicles, and vegetation. A key challenge in the live work zone deployments was lack of access to high-elevation locations for antenna mounting, and a general lack of power sources within active work zones. Across the test sites in the field, the typical blockages in the work zone that resulted in an effective range of C-V2X was approximately 500 meters, roughly one-fifth of the maximum measured line of sight range of 2260 meters. These findings are useful for estimating the number of CV2X units needed to achieve full coverage within work zone sites.

  PublisherDOI

• Workflows and Technical Challenges of Regional Multi-Resource Assessments in the U.S. Geological Survey

  2025-01-01

  articleSenior author
  PublisherDOI

• Characterizing the settling time for microscopic simulations of urban traffic for application to CAVs

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  Due to their cost efficiency, safety improvements, and repeatability, traffic simulators are an integral and widely used tool to study the performance of a traffic network with connected and/or autonomous vehicles (CAVs). Such simulations depend significantly on the choice of simulation parameters, and in most cases, it is up to the user to identify the appropriate setting for even a basic usage question: what duration should the simulation be run? It is generally understood that a simulation’s duration should be sufficient to ensure that the simulations are fully initialized wherein the user-chosen initial conditions of the simulation do not affect assessments of steady behavior. This duration is generally chosen based on the user’s practical experience. This paper presents a method for determining a simulation’s initialization interval by examining the settling time for traffic simulations, e.g. the time the simulation requires to reach steady-state conditions. In this work, steady-state behavior is measured via the cumulative means of the speeds of edges on a traffic network. The completion times of vehicle trips were also evaluated. Two examples of virtual and real-world networks were tested with Simulation of Urban MObility (SUMO) traffic simulator, and the results show that: i) for the given networks, the settling times were 0.63 and 0.73 hours for virtual and real-road networks respectively, and ii) the ratio between the mean of settling times and the mean of trip completion times for the real-world network is higher than the one for the virtual network, approximately 20 times versus 10 times longer respectively. An important insight of this work is that the required simulation time for the traffic simulation to converge to steady-state behavior is a large multiplier of the average trip time, and this duration is far longer than what is often seen in the literature.

  PublisherDOI

• Energy-Aware Predictive Motion Planning for Autonomous Vehicles Using a Hybrid Zonotope Constraint Representation

  2025-07-08 · 3 citations

  article

  Uncrewed aerial systems have tightly coupled energy and motion dynamics which must be accounted for by onboard planning algorithms. This work proposes a strategy for coupled motion and energy planning using model predictive control (MPC). A reduced-order linear time-invariant model of coupled energy and motion dynamics is presented. Constrained zonotopes are used to represent state and input constraints, and hybrid zonotopes are used to represent non-convex constraints tied to a map of the environment. The structures of these constraint representations are exploited within a mixed-integer quadratic program solver tailored to MPC motion planning problems. Results apply the proposed methodology to coupled motion and energy utilization planning problems for 1) a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and 2) an electric package delivery drone that must track waysets with both position and battery state of charge requirements. By leveraging the structure-exploiting solver, the proposed mixed-integer MPC formulations can be implemented in real time.

  PublisherDOI

• Assessment of carbon dioxide enhanced oil recovery and associated carbon dioxide retention resources of the United States

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Towards the Safe Operation of Autonomous Vehicles in Work Zones

  2025-06-22 · 1 citations

  article

  Autonomous vehicles (AVs) promise significant advances in transportation safety and efficiency. However, navigating roadway work zones, which can be rather complex and dynamic, remains a significant challenge. This paper presents the results of a large study that addresses the challenges, requirements, solutions and practical experiences of AVs driving safely through work zones. We begin by proposing a taxonomy of work zone scenarios and analyzing their structures and attributes. We next discuss the perception, routing, behavioral and path-planning requirements for AVs to safely navigate these scenarios. We then offer methods to meet these requirements and investigate the impact of range and AV speed on perception confidence levels for work zone detection. We evaluate our solutions in a co-simulation environment, on a closed test-track and on public roadways across more than 20 work zone scenarios specified by the Pennsylvania Department of Transportation (PennDoT). Video demonstrations illustrate the feasibility of safe and reliable navigation of AVs in a wide variety of work zones.

  PublisherDOI

• Approximating Infinite Impulse Response Filters with Finite Impulse Response Filters Using Allan Variance

  IFAC-PapersOnLine · 2025-01-01

  articleOpen accessSenior authorCorresponding

  This work presents the application of Allan VARiance (AVAR) to determine the order of an FIR filter whose output approximates that of an IIR filter. AVAR methods are typically used to analyze the variance of static windowed averages of data; prior recent work by the authors extended AVAR methods to include optimization of moving average filter designs, and more recently optimization of finite impulse response (FIR) filters. One of the main advantages of FIR over IIR filters is that the output of FIR depends only on the data size equal to one more than the filter order, whereas the data size influencing the output of the IIR filter increases with the length of data. A consequence of this is that the AVAR optimization of IIR filters remains an unsolved problem, but one that is solvable if IIR filters can be well approximated by FIR filter designs. In this work, the similarity between FIR and IIR filters is quantified using the normalized distance between the AVAR curves of error. This approximation is demonstrated through time-domain results of a signal with both low- and high-frequency noise contributions, namely drift (random walk) input corrupted by white noise. The results show that the AVAR-equivalent FIR filter output is similar to that of the IIR filter output if one chooses a sufficiently high FIR filter order. In addition, an iterative algorithm is presented to quickly estimate the necessary order of an FIR filter that is AVAR-equivalent to an IIR filter.

  PublisherDOI

• Extrinsic Calibration of Three-Dimensional LiDAR Using Sphere Targets and Differential-Corrected Global Positioning System

  Journal of Autonomous Vehicles and Systems · 2025-12-10

  articleSenior author

  Abstract This article presents an extrinsic calibration approach for locating the exact pose of a 3D light detection and ranging (LiDAR) sensor suitable for autonomous or mapping vehicles using sphere targets and multiple differential-corrected Global Positioning System (DGPS) antennas. The method employs constrained random sample consensus (RANSAC) for robust sphere detection, least-squares fitting for precise center estimation, and an iterative point-to-point alignment using singular value decomposition (SVD) for computing the pose transformations. Experimental validation using road lane markers demonstrates a mean positional accuracy of 3.6 cm, with a standard deviation of 1.3 cm and a maximum error of 7.1 cm. The methodology can achieve calibration either with single-scan data or by aggregating data across scans. These results confirm the method’s effectiveness in improving spatial data accuracy for autonomous navigation, HD map generation, and multisensor fusion applications.

  PublisherDOI

Recent grants

• CPS: Medium: Collaborative Research: Automated Discovery of Data Validity for Safety-Critical Feedback Control in a Population of Connected Vehicles

  NSF · $570k · 2019–2023

Frequent coauthors

• Karl Reichard

  26 shared

• Craig E. Beal

  19 shared

• Kshitij Jerath

  17 shared

• Alexander Brown

  Lafayette College

  16 shared

• Andrew G. Alleyne

  University of Minnesota

  16 shared

• Jesse Pentzer

  15 shared

• Robert D. Leary

  Pennsylvania State University

  14 shared

• Liming Gao

  11 shared

Labs

• PSMES BoardPI

  The PSMES board of directors is made up of elected officers, six to nine at large members, the ME department head, and a mechanical engineering faculty member.