Can I read Combating the Effects of Speed and Delays in End-to-end Self-driving on EtoBox?

Combating the Effects of Speed and Delays in End-to-end Self-driving by Tampuu, Ardi; Uduste, Ilmar; Roosild, Kristjan is a scholarly article available to read on EtoBox.

What is Combating the Effects of Speed and Delays in End-to-end Self-driving about?

In the behavioral cloning approach to end-to-end driving, a dataset of expert driving is collected and the model learns to guess what the expert would do in different situations. Situations are summarized in observations and the outputs are low or mid-level commands (e.g. brake, throttle, and steering; or trajectories). The models learn to match observations at time T to actions recorded at T or as simultaneously as possible. However, when deploying the models to the real world (or to an asynchronous simulation), the action predicted based on observations at time T gets applied at T + $\Delta$ T. In a variety of cases, $\Delta$ T can be considerable and significantly influence performance. We first demonstrate that driving at two different speeds is effectively two different tasks. Delays partially cause this difference and linearly amplify it. Even without computational delays, actuator delays and slipping due to inertia result in the need to perform actions preemptively when driving fast. The function mapping observations to commands becomes different compared to slow driving. We experimentally show that models trained to drive fast cannot perform the seemingly easier task of dri

Author

Tampuu, Ardi; Uduste, Ilmar; Roosild, Kristjan

Published

2023

Language

EN