Accurate prediction of driving scenes is essential for road safety and autonomous driving. Occupancy Grid Maps (OGMs) are commonly employed for scene prediction due to their structured spatial representation, flexibility across sensor modalities and integration of uncertainty. Recent studies have successfully combined OGMs with deep learning methods to predict the evolution of scene and learn complex behaviours. These methods, however, do not consider prediction of flow or velocity vectors in the scene. In this work, we propose a novel multi-task framework that leverages dynamic OGMs and semantic information to predict both future vehicle semantic grids and the future flow of the scene. This incorporation of semantic flow not only offers intermediate scene features but also enables the generation of warped semantic grids. Evaluation on the real-world NuScenes dataset demonstrates improved prediction capabilities and enhanced ability of the model to retain dynamic vehicles within the scene.

China's best known AI companies are Sensetime, Megvii, Cloudwalk, Yitu, ByteDance, and the BAT companies -- China's first generation of internet giant: Baidu, Alibaba and Tencent. But there's another giant of artificial intelligence that is rarely discussed in the same breath as the companies mentioned above. The state-owned electric utility monopoly State Grid Corporation of China (hereafter State Grid) is the largest utility company in the world, ranking second on the 2018 Fortune Global 500 List. Less celebrated is that State Grid was the only Chinese company ranked in the top 20 in artificial intelligence (AI) patent applicants, per the World Intellectual Property Organization. In an article (in Chinese) published last year titled "State Grid Corporation of China: A hidden giant in AI," Lǐ Shāng 李熵 gives a portrait of a company whose AI initiatives could change the world.