The impact of LLM chatbots on learning outcomes in advanced driver assistance systems
Our study investigates the efficacy of ChatGPT-assisted learning in enhancing the understanding of Advanced Driver Assistance Systems (ADAS) functionalities, comparing it against conventional paper-based learning methods. By employing multiple-choice questionnaires and the NASA Task Load Index to evaluate comprehension and cognitive load, we aimed to assess the impact of interactive Large Language Model (LLM)-driven learning on knowledge acquisition and learner satisfaction. Our findings indicate that participants who engaged with ChatGPT-based training scored higher (on average 11% higher) in correctness and experienced lower cognitive and physical demands, suggesting a more effective and less stressful learning process. This study contributes by highlighting ChatGPT’s potential to accommodate a wide range of learning preferences and improve the comprehension of complex systems or topics. This adaptability was evident across diverse educational backgrounds among young adult participants, showcasing the tool’s ability to bridge knowledge gaps more efficiently than conventional methods. Our research advocates the integration of LLM-driven tools in educational and policy-making frameworks to improve the effectiveness of teaching complex systems. This suggests broader applicability and necessitates further investigation into the scalability and effectiveness of ChatGPT-based training across different demographics and learning domains, potentially informing future educational strategies.
Importance of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies
In modern transportation systems, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies hold immense potential to revolutionize road safety, optimize traffic flow, and enhance accessibility. These systems, including functionalities such as Adaptive Cruise Control (ACC), Collision Avoidance (CA) and Blind Spot Assist (BSA), aim to assist drivers in managing the complexities of different driving tasks, thereby reducing the likelihood of accidents and improving overall road safety. The effectiveness of ADAS and AV technologies significantly depends on users' comprehension and proper use of these systems. Current studies reveal a significant underutilization of these features; only a few drivers consistently use systems like ACC, CA, and BSA. This limited engagement can be attributed to various factors, including a lack of perceived benefits, trust issues, functional limitations, a lack of legal framework, and an absence of user knowledge and experience.
Significance of ACC, CA, and BSA in mitigating crash incidents
This study focuses on ACC, CA, and BSA due to their demonstrated effectiveness in mitigating crash incidents and injuries, as highlighted by empirical evidence. These three functions represent a significant subset of commonly available ADAS features and are frequently the subject of studies having a substantial impact on safety. Specifically, the effective utilization of these three functions can decrease the number of car accidents by up to 89%.
