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This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.
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Background: Simulator-based driving assessments (SA) have recently been used and studied for various purposes, particularly for post-stroke patients. Automating such assessment has potential benefits especially on reducing financial cost and time. Nevertheless, there currently exists no clear guideline on assessment techniques and metrics available for SA for post-stroke patients. Therefore, this systematic review is conducted to explore such techniques and establish guidelines for evaluation metrics. Objective: This review aims to find: (a) major evaluation metrics for automatic SA in post-stroke patients and (b) assessment inputs and techniques for such evaluation metrics. Methods: The study follows the PRISMA guideline. Systematic searches were performed on PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library for articles published from January 1, 2010, to December 31, 2023. This review targeted journal articles written in English about automatic performance assessment of simulator-based driving by post-stroke patients. A narrative synthesis was provided for the included studies. Results: The review included six articles with a total of 239 participants. Across all of the included studies, we discovered 49 distinct assessment inputs. Threshold-based, machine-learning-based, and driving simulator calculation approaches are three primary types of assessment techniques and evaluation metrics identified in the review. Discussion: Most studies incorporated more than one type of input, indicating the importance of a comprehensive evaluation of driving abilities. Threshold-based techniques and metrics were the most commonly used in all studies, likely due to their simplicity. An existing relevant review also highlighted the limited number of studies in this area, underscoring the need for further research to establish the validity and effectiveness of simulator-based automatic assessment of driving (SAAD). Conclusions: More studies should be conducted on various aspects of SAAD to explore and validate this type of assessment.
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BACKGROUND: Driving simulators are effective tools to evaluate the driving abilities of patients with stroke. They can introduce various driving scenarios which will greatly benefit both the assessors and drivers. However, there is still no guidelines by which driving scenarios should be introduced in the driving assessment. OBJECTIVES: We conducted a systematic review to examine the utilization of driving scenarios and environments in the simulator-based driving assessment for patients with stroke. METHODS: A systematic review was conducted following PRISMA. We searched PubMed, Web of Science, ScienceDirect, ACM Digital Library, and IEEE Xplore Digital Library databases in January and June 2022 to identify eligible articles published since 2010. RESULTS: Our searches identified 1,614 articles. We included 12 studies that applied driving simulators to assess the driving performance of patients with stroke. The driving scenarios were categorized into three categories - vehicle controls scenarios, hazard perception scenarios, and trajectory planning scenarios - based on a certain set of driving abilities. The most common driving scenarios are simple navigation (n = 8) and emergency stop (n = 8). The most frequently used driving area is urban (n = 9), and a variety of roads and traffic conditions were found in the included studies. Only 2 studies applied weather conditions, such as the clear and sunny condition or the windy condition. CONCLUSION: It is recommended for future research to consider covering scenarios from the aforementioned three categories and further investigate the benefits of introducing complex weather conditions and localized traffic conditions in the driving assessment.