Artificial intelligence to recognise weather conditions
Autonomous vehicles cannot be operated without AI. Being able to read weather conditions will improve vehicle driving safety under adverse weather conditions.
Researchers at Oxford University’s Department of Computer Science, in collaboration with colleagues from the Bogazici University, Turkey, have developed a novel artificial intelligence (AI) system.
Yasin Almalioglu, who completed the research as part of his DPhil in the Department of Computer Science, said, “The difficulty for AVs to achieve precise positioning during challenging adverse weather is a major reason why these have been limited to relatively small-scale trials up to now. For instance, weather such as rain or snow may cause an AV to detect itself in the wrong lane before a turn, or to stop too late at an intersection because of imprecise positioning.”
To overcome this problem, Almalioglu and his colleagues developed a novel, self-supervised deep learning model for ego-motion estimation, a crucial component of an AV’s driving system that estimates the car’s moving position relative to objects observed from the car itself. The model brought together richly detailed information from visual sensors (which can be disrupted by adverse conditions) with data from weather-immune sources (such as radar), so that the benefits of each can be used under different weather conditions.
The model was trained using several publicly available AV datasets, which included data from multiple sensors such as cameras, lidar and radar under diverse settings, including variable light/darkness levels and precipitation. These were used to generate algorithms to reconstruct scene geometry and calculate the car’s position from novel data. Under various test situations, the researchers demonstrated that the model showed robust all-weather performance, including conditions of rain, fog and snow, as well as day and night.
The team anticipates that this work will bring AVs one step closer to safe and smooth all-weather autonomous driving, and ultimately a broader use within societies.
The full paper, “Deep learning-based robust positioning for all-weather autonomous driving”, is published in Nature Machine Intelligence. This will be published online at the following link once the embargo lifts: https://www.nature.com/articles/s42256-022-00520-5.
Source: University of Oxford
