Autonomous driving technology is advancing rapidly, particularly in vision-based approaches that use cameras to perceive the driving environment, which is the most human-like perception method. However, one of the key challenges that smart vehicles face is adapting to various weather conditions, which can significantly impact visual perception and vehicular control strategies. The ideal design for the latter is to dynamically adjust in real time to ensure safe and efficient driving, taking into account the prevailing weather conditions. In this study, we propose a lightweight weather perception model that incorporates multi-scale feature learning, channel attention mechanisms, and a soft voting ensemble strategy. This enables the model to capture various visual patterns, emphasize critical information, and integrate predictions across multiple modules for improved robustness. Benchmark comparisons are conducted using several well-known deep learning networks, including EfficientNet-B0, ResNet50, SqueezeNet, MobileNetV3-Large, MobileNetV3-Small, and LSKNet. Finally, using both public datasets and real-world video recordings from roads in Taiwan, our model demonstrates superior computational efficiency while maintaining high predictive accuracy. For example, our model achieves $98.07\%$ classification accuracy with only 0.4 million parameters and 0.19 GFLOPs, surpassing several well-known CNNs in computational efficiency. Compared with EfficientNet-B0, which has a similar accuracy ($98.37\%$) but requires over ten times more parameters and four times more FLOPs, our model offers a much lighter and faster alternative.

Various renewable energy sources, along with corresponding large-scale batteries, have been integrated into power grids, making renewable energy bidding and battery control critical in the real-time energy market. However, most bidding and control problems have been studied separately despite their accompanying impact on the total profit of renewable energy producers. Recently, a Reinforcement Learning (RL) strategy has been proposed to investigate renewable energy bidding and battery control jointly. It determines bidding values based on the battery’s error compensability and then applies additional battery control to the energy arbitrage process. Based on the same experimental scenarios, we present a method that incorporates the attention mechanism into long short-term memory reinforcement learning to increase total profits. We also consider various settings for our models to conduct a comprehensive survey. According to the experimental results, our method achieves significant performance gains over existing strategies, producing cumulative profits of over 400 k for solar energy and more than 200 k for wind energy. These results highlight the superior ability to balance real-time bidding precision and battery utilization efficiency, leading to higher profitability and stability in renewable energy market participation.