Doing cool things with data A typical self driving car starts with the perception system, which estimates the state of the surrounding environment, including landmarks and vehicles and pedestrians. The localization block compares what it has learned to a map to figure out where the vehicle is.

A review of BEV semantic segmentation as of 2020 — Updates: Add BEV feat stitching, 2021/01/31 Add PYVA, 2021/10/01 Add Panoptic BEV, 2021/10/04 TODO: Add BEV-Seg, CaDDN, FIERY, HDMapNet. I have also written an updated blog post regarding BEV object detection, especially with Transformers. Monocular BEV Perception with Transformers in Autonomous Driving A review of academic literature and industry practice as of late 2021towardsdatascience.com

I would like to explain the localisation concept learned through the udacity and hope this will be helpful for others. Expectation of this article to give you an idea about localisation in general and this can be foundation to my Particle Filters article. Localisation overview Self-driving cars use maps because these maps…