{"id":254824,"date":"2021-11-30T17:43:00","date_gmt":"2021-11-30T09:43:00","guid":{"rendered":"https:\/\/www.um.edu.mo\/news-and-press-releases\/campus-news\/detail\/52943-2\/"},"modified":"2021-12-29T12:05:33","modified_gmt":"2021-12-29T04:05:33","slug":"52943","status":"publish","type":"post","link":"https:\/\/www.um.edu.mo\/pt-pt\/news-and-press-releases\/campus-news\/detail\/52943\/","title":{"rendered":"Explorando o poder da IA na \u00e1rea da auto-condu\u00e7\u00e3o"},"content":{"rendered":"\n \n

Once only visible in science fiction films, self\u2011driving cars roaming streets in futuristic cities may enter your car park sooner than you think. To advance the technology behind such cars, the University of Macau (UM) and its partners from Macao and mainland China are conducting a major research project. Together, they have launched Macao\u2019s first\u2011ever self\u2011driving bus as a testing vehicle at the university.<\/p>

Tests Underway on an Unmanned Bus<\/p>

The self\u2011driving bus hit the road in October 2020. It has eight seats, allows six standees, and can travel up to 40 km per hour. Its launch is part of the ‘Key Technologies and Platforms for Collaborative Intelligence\u2010driven Autonomous Vehicles\u2019 project, which has been supported by the Macao Science and Technology Development Fund since 2019. The researchers come from the State Key Laboratory of Internet of Things for Smart City (University of Macau), the Shenzhen Institutes of Advanced Technology (SIAT) under the Chinese Academy of Sciences, the National University of Defense Technology, Baidu, and Shenzhen Haylion Technologies. Macao\u2019s telecommunications service provider CTM provides mobile network technology to support the project.<\/p>

The research team is headed by Prof Xu Chengzhong, dean of UM\u2019s Faculty of Science and Technology and chair professor of computer and information science. Prof Xu has studied self\u2011driving technology for over a decade. He worked with American automakers such as General Motors on the development of intelligent vehicles when he was a professor in Detroit. He has continued to explore the field since returning to China in 2011, and kick\u2011started this self\u2011driving car project in Macao after he joined UM in 2019.<\/p>

Self\u2011driving 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\u2011driving cars safer, Prof Xu\u2019s 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. <\/p>

Teaching Cars to Make Smart Decisions <\/p>

According to Prof Xu, Convolutional Neural Network (CNN), which is a popular type of machine learning model, is essential to self\u2011driving 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.<\/p>

The decision\u2011making power of a CNN largely depends on its training data. Most existing data for training self\u2011driving cars was collected in clear weather conditions, so it is easier for self\u2011driving 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\u2019t have enough data or time, nor can they foresee all the situations a car might encounter.<\/p>

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 \u2018transferred\u2019 model that has already been perfected for a similar task. <\/p>

With the self\u2011driving 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\u2011Initialising the Fully\u2011connected LayEr (RIFLE), which is used in the \u2018back propagation\u2019 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 the RIFLE algorithm at the prestigious International Conference on Machine Learning.<\/p>

Faster Training of Learning Models <\/p>

We can remove unnecessary parts of a CNN before retraining it for a new but similar task. Known as \u2018pruning’, the process can reduce the size of the original network and thus shorten the time required for training the new one. However, pruning sometimes sharply weakens the network\u2019s decision\u2011making power.<\/p>

To address this problem, researchers at UM, SIAT, and Baidu proposed a new pruning method called \u2018Attentive Feature Distillation and Selection\u2019 (AFDS). They used ADFS and some mainstream counterparts to prune a 101\u2011layer CNN, and then used the final products to identify objects from images stored in six databases. The results show that the AFDS\u2011pruned network remains very effective despite a 30 per cent cut on the amount of computation. Even when the amount of computation was reduced by 90 per cent, the network accuracy rate stood at 70 per cent, much higher than its competitors. The researchers have presented an academic paper about the AFDS method at the influential International Conference on Learning Representations.<\/p>

Self\u2011driving cars must be able to identify everything around them. CNNs can usually perform such tasks effectively, but they can be vulnerable when facing cyberattacks or unclear objects like road signs with graffiti. This can cause potentially fatal mistakes like misreading a stop sign as a give\u2011way sign. Therefore, Prof Xu\u2019s team has developed the \u2018LAFEAT\u2019 algorithm, which can make CNNs more robust to adversarial attacks or noise. The new algorithm has outperformed a dozen existing options in computer trials.<\/p>

In mid\u20112021, researchers at UM and SIAT presented the LAFEAT algorithm at the Conference on Computer Vision and Pattern Recognition, which has an acceptance rate of just 4.59 per cent. They also used the new algorithm to compete with 1680 teams from around the world at the CVPR Security AI Challenger, an algorithm competition jointly organised by the University of Illinois, Tsinghua University, and Alibaba Security. The team won a second prize, becoming the only winning team from Macao. <\/p>

Stepping into a Driverless Future <\/p>

Prof Xu\u2019s team is also studying technologies to enable better interactions between humans and self\u2011driving cars. For instance, they are designing natural language processing solutions that will allow cars to respond appropriately to spoken commands. Moreover, they are searching for ways to help cars adapt to the complicated road conditions in Macao, a city known for its narrow roads and abundance of motorbikes. <\/p>

So, when can we take our hands off the wheels? Prof Xu believes that many technological breakthroughs need to be achieved before self\u2011driving cars can finally go mainstream. A driverless future would also need major transformations in transport and telecommunications infrastructure as well as new legal rules \u2011 all requiring the concerted effort of various sectors of the community. Prof Xu says, \u2018We will of course continue to advance self\u2011driving technology, but by launching our bus on the UM campus, we also hope to raise the public\u2019s understanding of self\u2011driving cars, so that we can be better prepared for their deployment in Macao.\u2019<\/p>

<\/div>
Source: UMagazine<\/a> Issue 24<\/div>
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