Open Access

ARTICLE

A Perspective-Aware Cyclist Image Generation Method for Perception Development of Autonomous Vehicles

Beike Yu¹, Dafang Wang^1,*, Xing Cui², Bowen Yang¹

1 School of Mechatronics Engineering, Harbin Institute of Technology, Weihai, 264200, China
2 China North Artificial Intelligence & Innovation Research Institute, Beijing, 100000, China

* Corresponding Author: Dafang Wang. Email:

Computers, Materials & Continua 2025, 82(2), 2687-2702. https://doi.org/10.32604/cmc.2024.059594

Received 10 September 2024; Accepted 22 November 2024; Issue published 17 February 2025

Abstract

Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.

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Keywords

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