Educational Modules and Hands-On Activities

Transportation Engineering

Module 1: Traffic Control Devices

This module introduces the fundamental concepts of traffic control devices, including signs, signals, and pavement markings, with an emphasis on their role in regulating and guiding roadway users. Students explore standard conventions defined by the Manual on Uniform Traffic Control Devices (MUTCD), including color, shape, and functional classifications. The module also examines how these traditionally human-centered design principles must evolve to support autonomous vehicle (AV) systems while considering machine perception challenges such as sensor-based detection and interpretation under varying environmental conditions. Through this integrated perspective, students develop an understanding of how traffic control devices function within both conventional and automated transportation systems.

Module Materials:

Recorded Lecture	Travel Control Devices
Presentation Slide	Traffic Control Device (.pdf)
Instructor Notes	Traffic Control Devices - Instructor Notes (.pdf)
Quiz	Traffic Control Devices - Quiz (.pdf)

 

Module 2: Traffic Signal Timing and Phasing

This module introduces the fundamental principles of traffic signal operations, phasing, and timing, with a focus on how signals manage conflicts and allocate right-of-way at intersections. Students explore different signal control strategies, including pre-timed, actuated, and demand-responsive systems, as well as left-turn treatments and phasing configurations. The module covers essential timing components such as cycle length, green time allocation, yellow change intervals, and all-red clearance intervals, supported by analytical methods such as critical flow ratios and lost time calculations. Emphasis is placed on engineering judgment in the absence of strict national timing standards, highlighting how practitioners balance safety, efficiency, and multimodal needs. Through this framework, students develop the ability to design and evaluate basic signal timing plans for real-world intersection conditions.

Module Materials:

Recorded Lecture	Traffic Signals Phasing and Timing
Presentation Slide	Traffic Signals Phasing and Timing Slides (.pdf)
Instructor Notes	Traffic Signal - Instructor Notes (.pdf)
Quiz	Traffic Signal - Quiz (.pdf)

 

 

Module 3: Traffic Flow Theory

This module introduces the fundamental principles of traffic flow theory, focusing on the key parameters of speed, flow, and density and their interrelationships. Students explore multiple levels of traffic stream analysis such as microscopic, mesoscopic, and macroscopic and examine how individual driver behavior influences overall traffic patterns. The module emphasizes quantitative methods for measuring and analyzing traffic variables, including time-mean and space-mean speeds, headways, and spacing. Using the fundamental traffic flow relationship and Greenshield’s linear model, students develop an understanding of how traffic conditions evolve under varying demand levels. The module also compares theoretical models with real-world traffic data, highlighting limitations of classical models and exploring how emerging technologies such as autonomous vehicles may enhance traffic efficiency and stability.

Module Materials:

Recorded Lecture	Fundamentals of Traffic Flow
Presentation Slide	Traffic Flow Slides (.pdf)
Instructor Notes	Traffic Flow - Instructor Notes (.pdf)
Quiz	Traffic Flow - Quiz (.pdf)

 

Module 4: Roadway Geometric Design

This module introduces the fundamental principles of highway geometric design, emphasizing the role of roadway geometry in ensuring safety and operational efficiency. Students explore the three-dimensional nature of roadway alignment (horizontal, vertical, and cross-sectional) and learn how these components are represented and analyzed in engineering practice. The module covers key design concepts such as stopping sight distance, passing sight distance, and the geometric characteristics of horizontal and vertical curves. Using American Association of State Highway and Transportation Officials (AASHTO) guidelines, students develop an understanding of how design decisions are informed by driver behavior, vehicle characteristics, and environmental conditions. The module also examines how emerging technologies, including autonomous vehicles, may influence future geometric design standards and requirements.

Module Materials:

Recorded Lecture	Highway Geometric Design
Presentation Slide	Geometric Design Slides (.pdf)
Instructor Notes	Geometric Design - Instructor Notes (.pdf)
Quiz	Geometric Design - Quiz (.pdf)

 

Module 5: Supplementary Topics

This module introduces three important supplementary topics in traffic engineering: human factors, level of service (LOS), and intelligent transportation systems (ITS). Students examine how human abilities, limitations, and behavior influence transportation system design, with emphasis on concepts such as mental models, signifiers, constraints, and feedback. The module also introduces level of service as a qualitative measure of roadway performance, exploring its application across different facility types including freeways, multilane highways, and two-lane highways under both ideal and prevailing conditions. Finally, the module provides an overview of ITS and emerging technologies such as vehicle-to-everything (V2X) communication, highlighting their role in improving safety, efficiency, and sustainability. Through this integrated perspective, students develop an understanding of how human-centered design, performance evaluation, and advanced technologies interact in modern and future transportation systems.

Module Materials:

Recorded Lecture	Supplementary Topics in Traffic Engineering
Presentation Slide	Supplementary Topics Slides (.pdf)
Instructor Notes	Supplementary Topics - Instructor Notes (.pdf)
Quiz	Supplementary Topics - Quiz (.pdf)

 

Electrical Engineering

Module 1: Introduction to Signals and Systems

This module introduces the fundamental concept of signals as time- or space-varying information and systems as entities that transform inputs into outputs. It connects everyday examples, such as traffic flow, vehicle speed, and sensor data to engineering representations used in autonomous vehicles. The module explains different signal types (e.g., continuous, discrete, digital, periodic) and highlights the impact of noise and disturbances on system performance. It also emphasizes how signals and systems form the foundation for perception, control, and communication subsystems in autonomous transportation.

 

Module Materials:

Recorded Lecture	Introduction to Signals and Systems
Presentation Slide	Introduction to Signals and Systems - Slide (.pdf)
Instructor Notes	Introduction to Signals and Systems - Instructor Notes (.pdf)
Quiz	Introduction to Signals and Systems - Quiz (.pdf)

 

Module 2: Sensor Technologies in Autonomous Vehicles

This module provides an overview of the primary sensing technologies used in autonomous vehicles, including LiDAR, Radar, cameras (2D/3D/thermal), ultrasonic sensors, and GPS/IMU systems. It explains how each sensor operates, their strengths and limitations, and their roles in perception, localization, and safety. The slides also emphasize that no single sensor is sufficient, highlighting the need for redundancy and complementary sensing to ensure reliability under different environmental conditions.

Module Materials:

Recorded Lecture	Sensor Technologies in Autonomous Vehicles
Presentation Slide	Sensor Technologies - Slides (.pdf)
Instructor Notes	Sensor Technologies - Instructor Notes (.pdf)
Quiz	Sensor Technologies - Quiz (.pdf)

 

Module 3: Sensor Fusion and Artificial Intelligence

The module focuses on how multiple sensors are combined (i.e., sensor fusion) to create a reliable understanding of the environment and how artificial intelligence enables perception and decision-making. It explains that individual sensors have limitations but combining them improves robustness and safety. The module introduces key fusion algorithms (e.g., Kalman filters) and AI concepts such as machine learning, neural networks, and convolutional neural networks (CNNs), which are widely used for object detection and scene understanding.

Module Materials:

Recorded Lecture	Sensor Fusion and Artificial Intelligence
Presentation Slide	Sensor Fusion - Slide (.pdf)
Instructor Notes	Sensor Fusion - Instructor Notes (.pdf)
Quiz	Sensor Fusion - Quiz (.pdf)

 

Module 4: Control Systems 

This module explains how control systems enable autonomous vehicles to execute planned motions by regulating steering, speed, and braking through feedback mechanisms. It introduces the “See–Think–Act” architecture and shows how control integrates with perception and planning. Key concepts include feedback control, vehicle dynamics, tracking errors, and common control techniques such as PID and Model Predictive Control (MPC). The module also highlights how roadway design (civil engineering factors) directly influences control performance and safety.

Module Materials:

Recorded Lecture	Control Systems
Presentation Slide	Control Systems - Slides (.pdf)
Instructor Notes	Control Systems - Instructor Notes (.pdf)
Quiz	Control Systems - Quiz (.pdf)

 

Module 5: Communication System

The module introduces communication systems as a critical component enabling data exchange within the vehicle and with external entities. It covers both wired and wireless communication protocols, internal vehicle networks (e.g., CAN bus), and external communication frameworks such as V2V, V2I, V2P, and V2N. The module highlights how communication enhances safety, mobility, and efficiency by enabling cooperative driving and real-time traffic management. It also discusses cybersecurity and privacy challenges associated with connected vehicle systems.

Module Materials:

Recorded Lecture	Communication Systems
Presentation Slide	Communication Systems - Slide (.pdf)
Instructor Notes	Communication Systems - Instructor Notes (.pdf)
Quiz	Communication Systems - Quiz (.pdf)

 

Hands-On Activities

Activity 1: Hardware Tests

This activity focuses on verifying that all QCar system components, such as CSI cameras, LiDAR, motors, and sensors, are functioning correctly before running advanced experiments. Students perform connectivity checks, deploy real-time models, and test individual subsystems like cameras and LiDAR visualization. The goal is to ensure proper system setup and build confidence in interacting with hardware and real-time systems.

Module Materials:

• Lab Manual: Hardware Test MATLAB 

 

Activity 2: RGBD Imaging

In this activity, students capture synchronized RGB and depth images using an Intel RealSense camera and apply image processing techniques to detect a red stop sign. The system then estimates the distance to the detected object using corresponding depth data. The goal is to introduce students to depth sensing and demonstrate how visual and depth information can be combined for object detection and distance estimation.

Module Materials:

• Lab Manual: RGB Imaging MATLAB

 

Activity 3: LiDAR Point Cloud Generation

In this activity, students collect LiDAR data and convert it into a 2D/3D point cloud representation of the environment. The system processes distance and angle data to generate spatial maps of surroundings. Students understand how autonomous vehicles perceive their environment using LiDAR and build spatial awareness through mapping.

Module Materials:

• Lab Manual: LiDAR Point Cloud MATLAB

 

Activity 4: Manual Drive 

This activity allows students to manually control the QCar using a gamepad while monitoring system parameters such as speed, battery level, and power consumption. The goal is to familiarize students with vehicle dynamics, control inputs, and system feedback in a controlled environment.

Module Materials:

• Lab Manual: Manual Drive MATLAB

 

Activity 5: Lane Following

Students implement a vision-based lane-following system using camera input, color thresholding, and image processing to detect lane markings. The system also incorporates obstacle detection using depth data and adjusts speed accordingly. The goal is to develop understanding of perception, decision-making, and control algorithms for autonomous driving.

Module Materials:

• Lab Manual: Lane Following MATLAB

 

Activity 6: 360° Vision

This activity captures images from four cameras mounted around the vehicle and stitches them into a single panoramic view for full environmental awareness. The objective is to demonstrate multi-camera systems and provide a comprehensive view of surroundings, which is critical for safe navigation and situational awareness.

Module Materials:

• Lab Manual: 360 Vision MATLAB

 

Supplementary Materials

Industry Advisory Board Survey	Industry Advisory Board Survey (.pdf)
Student Sample Hands-On Activity Report	WAVE Student Sample Report (.pdf)