[Paper Review] Teaching Autonomous Driving Using a Modular and Integrated Approach

Jie Tang, Shaoshan Liu+5 more|arXiv (Cornell University)|Feb 22, 2018

Real-time simulation and control systems7 references3 citations

TL;DR

This paper presents a modular and integrated pedagogical approach to teaching autonomous driving by organizing core technologies into distinct, self-contained modules supported by a textbook and online multimedia lectures. The method enables learners with diverse technical backgrounds to progressively master complex systems through familiar concepts before integrating modules via hands-on experimental platforms, demonstrating high student engagement and rapid progress across academic and industry training contexts.

ABSTRACT

Autonomous driving is not one single technology but rather a complex system integrating many technologies, which means that teaching autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. This not only fails to provide a comprehensive coverage, but also sets a high entry barrier for students with different technology backgrounds. In this paper, we present a modular, integrated approach to teaching autonomous driving. Specifically, we organize the technologies used in autonomous driving into modules. This is described in the textbook we have developed as well as a series of multimedia online lectures designed to provide technical overview for each module. Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other. To verify this teaching approach, we present three case studies: an introductory class on autonomous driving for students with only a basic technology background; a new session in an existing embedded systems class to demonstrate how embedded system technologies can be applied to autonomous driving; and an industry professional training session to quickly bring up experienced engineers to work in autonomous driving. The results show that students can maintain a high interest level and make great progress by starting with familiar concepts before moving onto other modules.

Motivation & Objective

• To address the challenge of teaching autonomous driving as a complex, multidisciplinary system rather than isolated technologies.
• To lower the entry barrier for students with varied technical backgrounds by starting with familiar, foundational concepts.
• To develop a scalable, modular curriculum that integrates theory, multimedia instruction, and practical system-level experimentation.
• To validate the approach across diverse educational and training contexts, including introductory courses, embedded systems classes, and professional training.

Proposed method

• Organizing autonomous driving technologies into discrete, self-contained modules covering perception, planning, control, and system integration.
• Creating a companion textbook and a series of multimedia online lectures to provide technical overviews for each module.
• Designing experimental platforms that allow students to integrate and test modules in a simulated or real-world vehicle environment.
• Using a progressive learning path: students first master individual modules before engaging in system-level integration.
• Applying the framework in three distinct case studies: an introductory course, an embedded systems course module, and a professional training session.
• Evaluating student engagement and progress through qualitative and quantitative feedback across all case studies.

Experimental results

Research questions

• RQ1 How can a modular and integrated teaching approach improve student comprehension of autonomous driving systems across diverse technical backgrounds?
• RQ2 What impact does starting with familiar, foundational concepts have on student motivation and learning progression in autonomous driving education?
• RQ3 To what extent can a standardized modular curriculum be effectively adapted to different educational settings, including introductory courses and professional training?
• RQ4 How effective is the integration of hands-on experimental platforms in reinforcing theoretical knowledge and system-level understanding?
• RQ5 What evidence supports the scalability and reusability of this teaching framework in both academic and industrial training environments?

Key findings

• Students in the introductory course maintained high interest levels and demonstrated significant learning progress despite limited prior technical exposure.
• The modular approach enabled students with basic technology backgrounds to successfully engage with and understand complex autonomous driving systems.
• Incorporating the curriculum into an existing embedded systems course allowed students to apply embedded system principles to real-world autonomous driving applications.
• Industry professionals in the training session achieved rapid proficiency in autonomous driving concepts, indicating the framework's effectiveness for upskilling experienced engineers.
• Feedback from all case studies confirmed the approach’s scalability, adaptability, and strong learner engagement across diverse educational and professional contexts.