🚗How to get rid of lidar sensors and improve the quality of self-driving cars - research by ML specialists from MIT
Modern self-driving cars are driven with a giant rotating cylinder on the roof. It is a lidar sensor that sends pulses of infrared light and measures the time it bounces off objects to create a 3D map of points around the vehicle. However, this 3D data is huge and computationally intensive. For example, a typical 64-channel sensor delivers over 2 million points per second. Due to the extra spatial dimension, modern 3D models require 14 times more computation during output than their 2D counterparts. Therefore, for efficient navigation, engineers have to convert data to 2D, as a result of which some information is lost.
A team of DS specialists from MIT is creating a new ML automatic driving system that will do autonomous navigation using only raw 3D point cloud data and low-resolution GPS maps like smartphones. They even had to develop new deep learning components to use the GPU more efficiently and drive cars in real time. During testing, the system reduced the frequency of transmission of control of the machine to the human driver and could even withstand severe sensor failures. This hybrid evidence-based approach, which combines various control predictions together to arrive at the optimal choice of motion planning, performed better than traditional 3D lidar. And by combining control predictions according to model uncertainty, the system can adapt to unexpected events. The main components of the system are a driving platform without high definition 3D maps, an ML system and a deep 3D learning solution that optimizes neural architecture and inference library. Further, the team plans to develop the project, working out unfavorable weather conditions and dynamic interaction with other vehicles.
https://www.csail.mit.edu/news/more-efficient-lidar-sensing-self-driving-cars