Steve Viscelli: Trucking and the Decline of the American Dream | Lex Fridman Podcast #237
Nov 4, 2021
Steve Viscelli is a former truck driver and now an economic sociologist at University of Pennsylvania studying freight transportation, including autonomous trucks.
Outline:
0:00 - Introduction
0:44 - Ethnography
12:57 - Challenges of driving a truck
31:36 - Trucking industry: State of affairs
1:04:41 - Future of autonomous trucks
1:30:57 - Solving the automated truck dilemma
2:02:52 - Role of society in automated trucking
2:30:01 - Tesla and revolutionizing the trucking industry
2:49:41 - Hope and final thoughts
Article "35 under 35: Meet the rising stars of the self-driving industry, from companies like Waymo, Cruise, and Zoox"
by Alexa St. John and Nora Naughton
February 10, 2022
These are the self-driving cars to watch in 2022
Feb 22, 2022
You can’t lean back and strap on your Oculus behind the wheel just yet, but we’re getting tantalizingly closer to that day. Cooley shows you the autonomous cars he’s most excited about seeing this year.
UW Off-road autonomous driving - Off Trail Run
Apr 3, 2022
This video shows a test of autonomous off-road driving conducted by University of Washington researchers from the Paul G. Allen School of Computer Science and Engineering and the Applied Physics Laboratory. This test is on the "Offtrail" course, a short run that features unstructured terrain with scattered tree obstacles, very muddy forest trails, a number of drainage ditches and mounds, and a hairpin turn. The course in this video is defined by five waypoints, and the goal of this test is to run the course aggressively with no human interventions. The autonomous vehicle uses only onboard sensors and compute and does not reference GPS or predefined maps for localization.
Compared to on-road driving that is engineered to be simple, repeatable, and predictable, off-road terrain lacks man-made structure and contains challenging features including vegetation, uneven and low-friction surfaces, reduced visibility, obstacles, and rapidly changing terrain surface properties. When navigating at high-speed in these conditions, existing approaches to perception, planning, and control fail.
TartanDrive: Roboticists go off road
May 23, 2022
"Roboticists Go Off Road To Compile Data That Could Train Self-Driving ATVs"Researchers from Carnegie Mellon University took an all-terrain vehicle on wild rides through tall grass, loose gravel and mud to gather data about how the ATV interacted with a challenging, off-road environment.
They drove the heavily instrumented ATV aggressively at speeds up to 30 miles an hour. They slid through turns, took it up and down hills, and even got it stuck in the mud — all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors.
The resulting dataset, called TartanDrive, includes about 200,000 of these real-world interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.
TartanDrive Dataset Likely Largest for Off-Road Environments
by Byron Spice
May 25, 2022
Posting Permissions
Reply With Quote
Социальные закладки