Testing and validation of the functionalities and safety of automated vehicles shifted from a distance-based to a scenario-based method in the past decade. A number of domain-specific languages and systems were developed to support scenario-based testing. The aim of this paper is to review and compare the features and characteristics of the major scenario description languages and systems (SDLS). Each of them is designed for different purposes and with different goals; therefore, they have their strengths and weaknesses. Their characteristics are highlighted with an example nontrivial traffic scenario that we designed. We also discuss some directions for further development and research of these SDLS.

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (\(4.3 \times 10^5\)), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

Autonomous driving technology has developed rapidly in recent years, and computer vision has shown significant role in automated vehicles. Researchers use the combination of computer vision and deep learning to improve the speed and accuracy of road element detection technology. This article focuses on the semantic segmentation and road detection elements, especially in lane line segmentation and signal light detection. It summarizes the visual inspection technology and a series of visual perception algorithms based on deep learning. Although semantic segmentation and target detection technologies in general scenarios are already very mature, they are not ideal when applied to autonomous driving environments, especially where segmentation and detection are performed simultaneously. Therefore, this paper builds a fusion network of lane line segmentation and signal light detection suitable for autonomous driving scenarios.

The application of V2X technology has promoted autonomous driving from autonomous vehicles to connected vehicles, so various V2X test methods have gradually become an important research direction. Generally V2X testing for the cooperative connected and automated mobility includes a test framework, infrastructure deployment, and use cases. These three parts of the V2X testing affect each other and determine the performance of different test methods. Firstly, this article reviews the related research of V2X performance testing and analyzes the usual techniques in the testing framework. Then RSU deployment is introduced as an example of facility deployment. Finally, the typical use cases and their performance indicators are summarized.

In February 2020, Beijing, China andCalifornia, USA respectively released road test reports of 2019 for autonomous vehicles. Beijing and California respectively represent the highest level of testing and application of autonomous vehicles in the two countries. This article will compare the test items, evaluation criteria and technical defects of each autonomous vehicle company in the road test reports of China and the United States, also analyze the existing problems, and propose an idea for the construction of a comprehensive test site for autonomous vehicles. This article aims to solve the prominently exposed problems in decision-making and planning in autonomous vehicles with DQN algorithm-base vehicle fleet, and to look forward to the future development trend of autonomous driving testing.