Recent advancement in statistical learning and computational ability has enabled autonomous vehicle technology to develop at a much faster rate and become widely adopted. While many of the architectures previously introduced are capable of operating under highly dynamic environments, many of these are constrained to smaller-scale deployments and require constant maintenance due to the associated scalability cost with high-definition (HD) maps. HD maps provide critical information for self-driving cars to drive safely. However, traditional approaches for creating HD maps involves tedious manual labeling. As an attempt to tackle this problem, we fuse 2D image semantic segmentation with pre-built point cloud maps collected from a relatively inexpensive 16 channel LiDAR sensor to construct a local probabilistic semantic map in bird's eye view that encodes static landmarks such as roads, sidewalks, crosswalks, and lanes in the driving environment. Experiments from data collected in an urban environment show that this model can be extended for automatically incorporating road features into HD maps with potential future work directions.

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An enhanced catalytic activity and improved reusability were achieved by applying facile KNO3 modification during the synthesis of a copper‐manganese mixed oxide (CuMnO). Upon KNO3 modification, the characteristic finishing temperature (Tf) of catalytic soot combustion was decreased from 360 °C for CuMnO to 338 °C for the K‐modified product CuMnO(K). Moreover, this Tf value for CuMnO(K) remained remarkably low (346 °C) even after five runs of an activity test. The excellent performance of CuMnO(K) is attributed to its well‐dispersed nanoparticle morphology, which is totally different from the microspherical features of CuMnO and is beneficial for its sufficient contact with the soot particles. Additionally, an interesting phase evolution of CuMnO(K) was observed for the first time from Cu1.5Mn1.5O4 to the mixed phases of CuO, K2Mn4O8, and MnOx during the consecutive test runs. These mixed phases are believed to be responsible for the enhancement and stabilization of the catalytic activity. Furthermore, the mechanism for the morphology transformation upon KNO3 modification and for the phase evolution during soot combustion was investigated.