Predicting Desired Temporal Waypoints from Camera and Route Planner Images using End-To-Mid Imitation Learning The Ohio State University

Format:

Book

Conference/Event

Author/Creator:

Arul Doss, Aravind Chandradoss, author.

Contributor:

Güvenç, Levent

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans' driving behavior, in particular, their use of vision for driving' and map for navigating'. The notion is that we humans use our vision to drive' and sometimes, we also use a map such as Google/Apple maps to find direction in order to navigate'. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving. Also, even after decades of research, the reasonable place for mid' in the End-to-End approach, as well as, the trade-off between data-driven and math-based approach is not fully understood. Therefore, we focused on the end-to-mid learning approach and tried to identify the reasonable place for mid' in the end-to-end pipeline

Notes:

Vendor supplied data

Publisher Number:

2021-01-0088

Access Restriction:

Restricted for use by site license