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- Babu, Md Abu Ahammed, 1994, et al.
(författare)
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Impact of Image Data Splitting on the Performance of Automotive Perception Systems
- 2024
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Ingår i: Lecture Notes in Business Information Processing. - 1865-1356 .- 1865-1348. ; 505 LNBIP, s. 91-111
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Konferensbidrag (refereegranskat)abstract
- Context: Training image recognition systems is one of the crucial elements of the AI Engineering process in general and for automotive systems in particular. The quality of data and the training process can have a profound impact on the quality, performance, and safety of automotive software. Objective: Splitting data between train and test sets is one of the crucial elements in this process as it can determine both how well the system learns and generalizes to new data. Typical data splits take into consideration either randomness or timeliness of data points. However, in image recognition systems, the similarity of images is of equal importance. Methods: In this computational experiment, we study the impact of six data-splitting techniques. We use an industrial dataset with high-definition color images of driving sequences to train a YOLOv7 network. Results: The mean average precision (mAP) was 0.943 and 0.841 when the similarity-based and the frame-based splitting techniques were applied, respectively. However, the object-based splitting technique produces the worst mAP score (0.118). Conclusion: There are significant differences in the performance of object detection methods when applying different data-splitting techniques. The most positive results are the random selections, whereas the most objective ones are splits based on sequences that represent different geographical locations.
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| 2. |
- Chand, Sivajeet, 1998, et al.
(författare)
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Comparing Word-Based and AST-Based Models for Design Pattern Recognition
- 2023
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Ingår i: ACM International Conference Proceeding Series. ; , s. 44-48
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Konferensbidrag (refereegranskat)abstract
- Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. The models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. This study demonstrates how two language models-Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. The results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.
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| 3. |
- Kumar Pandey, Sushant, 1990, et al.
(författare)
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Design Patterns Understanding and Use in the Automotive Industry: An Interview Study
- 2024
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Ingår i: Lecture Notes in Computer Science. - 0302-9743 .- 1611-3349. ; 14483, s. 301-319
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Konferensbidrag (refereegranskat)abstract
- Automotive software is increasing in complexity, leading to new challenges for designers and developers. Design patterns, which offer reusable solutions to common design problems, are a potential way to deal with this complexity. Although design patterns have received much focus in academic publications, it is not clear how they are used in practice. This paper presents an interview-based study that explores the use of design patterns in the automotive industry. The study findings reveal how automotive practitioners view and use design patterns in their software designs. Our study revealed that industry experts have a view of design patterns which often differs from the academic views. They use design patterns in combination with architecture guidelines, principles, and frameworks. Instead of the academic focus on the design patterns, industry professionals focus on the design, architectural tactics, and standards. Such findings highlight the need for a more nuanced understanding of the concept and practical applications of design patterns within the context of industrial software engineering practices.
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