Description

Autonomous vehicles (AVs) are expected to transform transportation systems and reshape workforce needs across engineering and related fields. Although existing AV workforce development efforts often emphasize electrical engineering, computer science, and mechanical engineering, transportation engineering remains underrepresented despite its importance to infrastructure, traffic operations, safety, and mobility integration. This study addresses that gap through the development, implementation, and evaluation of an interdisciplinary certificate program, Workforce for Autonomous Vehicle Engineering (WAVE), at California State University, Sacramento. The program was designed to bridge transportation engineering and electrical engineering through a two-step curriculum model consisting of theoretical educational modules and hands-on laboratory activities. Transportation engineering modules covered traffic control devices, traffic signal timing and phasing, traffic flow theory, roadway geometric design, and supplementary topics such as human factors, level of service, and intelligent transportation systems. Electrical engineering modules addressed signals and systems, sensing technologies, sensor fusion and artificial intelligence, control systems, and communication systems. The program was delivered over seven weeks to 53 civil and electrical engineering students through a customized Canvas platform and laboratory sessions using the QCar AV platform. Program effectiveness was evaluated using a three-layer framework: pre- and post-surveys, student program evaluation, and review by a panel of industry experts. Results showed substantial improvement in both student competence and confidence, strong positive student perceptions of the curriculum and laboratory experience, and strong validation from industry experts regarding the program’s interdisciplinary structure and workforce relevance. The findings demonstrate that integrating transportation engineering with electrical engineering through theory-based and experiential learning provides an effective model for preparing students for emerging careers in the AV workforce.

Publication Date

7-2026

Publication Type

Report

Topic

Transportation Engineering, Transportation Technology, Workforce and Labor

Digital Object Identifier

10.31979/mti.2026.2550

MTI Project

2550

Mineta Transportation Institute URL

https://transweb.sjsu.edu/research/2550-Autonomous-Transportation-Electrical-Civil-Engineering

Keywords

Autonomous vehicles, Workforce development, Education and training, Interdisciplinary studies, Intelligent transportation systems (ITS)

Disciplines

Transportation

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