A full day tutorial covering all aspects of self-driving. This tutorial will provide the necessary background for understanding the different tasks and associated challenges, the different sensors and data sources one can use and how to exploit them, as well as how to formulate the relevant algorithmic problems such that efficient learning and inference is possible. We will first introduce the self-driving problem setting and the existing solutions both top down from a high level perspective and bottom up from technology and algorithm specific manner in a detailed fashion. We will then extrapolate from the state of the art and discuss where the challenges and open problems are, and where we need head towards to provide a scalable, safe and affordable self-driving solution.

Program & Recordings

All times are in Pacific Daylight Time (PDT).


8:45am 9:00am
9:00am 9:20am
9:25am 9:30am
Speaker: Raquel Urtasun
2.1 End-to-end approaches
2.2 Modular approaches
9:30am 10:00am
10:00am 10:15am

Break ☕

10:15am 10:50am
12:00pm 1:00pm

Lunch Break 🥪


1:00pm 1:25pm
Speaker: Ersin Yumer
7.1 Labeling and Data Collection
7.2 Benchmarking
1:25pm 2:05pm
Speakers: Siva Manivasagam, Simon Suo, and Xinchen Yan
8.1 Behavior simulation
8.2 Lidar simulation
8.3 Camera simulation
2:05pm 2:15pm

Break ☕

2:15pm 2:50pm
Speaker: Raquel Urtasun, Quinlan Sykora

2:50pm 3:30pm
Speakers: Shenlong Wang, Julieta Martinez, and Andrei Bârsan
Understand how self-driving vehicles robustly establish their precise position within HD maps in order to leverage them for safe and efficient autonomous driving.
3:30pm 4:00pm

Discussion and Q/A