Automated driving functions, like highway driving and parking assist, are increasingly getting deployed in high-end cars with the goal of realizing self-driving car using Deep learning techniques like convolution neural network (CNN), Transformers. Camera based perception, Driver Monitoring, Driving Policy, Radar and Lidar perception are few of the examples built using DL algorithms in such systems. Traditionally custom software provided by silicon vendors are used to deploy these DL algorithms on devices. This custom software is very optimal for supported features (limited), but these are not flexible enough for evaluating various deep learning model architecture types quickly. In this paper we propose to use various open-source deep learning inference frameworks to quickly deploy any model architecture without any performance/latency impact. We have implemented this proposed solution with three open-source inference frameworks (Tensorflow Lite, TVM/Neo-AI-DLR and ONNX Runtime) on Linux running in ARM.
Research collaboration between academic researchers from universities, organizations and government local city departments can be tremendously useful to all institutions, but these collaborations involve a wide range of skill sets, making them difficult to establish and manage. For many local government city departments, finding research professionals and collaborators to solve community problems remains a big challenge. The information of research opportunities is either posted on the individual city department website or researchers are approached through a personal relationship with city department officials. As a result, a researcher interested in working with the city department would have to either navigate various websites or try to build a personal contact with city department officials. In this paper, we will look at the relevance of community research partnerships as well as the barriers that prevent them. We will also demonstrate the development of, Research Partnership Portal, a collaborative platform for academic researchers, organizations, and government city departments in San Antonio. This portal will assist academic researchers and organizations in collaborating with government city departments and using current administrative data to produce effective answers to community concerns.