COVER STORY • 封面專題 2021 UMAGAZINE 24 • 澳大新語 10 Self‑driving research has proliferated across the world. Today, cars can already drive themselves smoothly in controlled environments. However, Prof Xu says that we are still far from having fully autonomous cars that can safely navigate in extreme weather conditions and other unpredictable situations in the real world. In making self‑driving cars safer, Prof Xu’s team has seen some encouraging results. In 2019 alone, they published 22 papers on related technologies in leading academic journals and presented some other papers at top international conferences. Teaching Cars to Make Smart Decisions According to Prof Xu, Convolutional Neural Network (CNN), which is a popular type of machine learning model, is essential to self‑driving cars. A CNN loosely mimics the way a human thinks. Given a large image dataset for it to learn from, a CNN can be trained to detect objects such as road signs, vehicles, and pedestrians from new images. The decision‑making power of a CNN largely depends on its training data. Most existing data for training self‑driving cars was collected in clear weather conditions, so it is easier for self‑driving cars to detect objects in good weather, but they may make serious mistakes in adverse weather conditions such as typhoons and snow. In theory, scientists can bring in more data to train a CNN (or other machine learning model) for every scenario. In practice, however, they probably won’t have enough data or time, nor can they foresee all the situations a car might encounter. One of the solutions is transfer learning, which works a bit like a cyclist learning to ride a motorcycle. By drawing on their experience, cyclists may find it easier to balance themselves on two wheels, without starting from scratch. Similarly, in the absence of enough data or time, researchers can train a machine learning model based on a ‘transferred’ model that has already been perfected for a similar task. With the self‑driving car project, researchers at UM and Baidu have proposed a new way to help CNNs based on transfer learning to accurately detect objects like stop signs. Their algorithm is called Re‑Initialising the Fully‑connected LayEr (RIFLE), which is used in the ‘back propagation’ process in the training of CNNs. The researchers used a CNN trained with the new algorithm to classify, detect, and segment tens of thousands of images. It turns out that this CNN outperforms its rivals trained with some mainstream algorithms. The team has presented a paper about 研究人員可在設於智慧城市物聯網國家重點實驗室（澳門大學）的實驗平台用模型車輛測試自動駕駛技術 This testbed in the State Key Laboratory of Internet of Things for Smart City (University of Macau) allows researchers to test their self‑driving strategies on mini‑cars in a mock urban scenario
RkJQdWJsaXNoZXIy MTQ1NDU2Ng==