Autonomous-Vehicle Ridesharing Remains in Uber’s Sightlines
Uber hasn’t set a timeline for deploying autonomous vehicles (AVs) on its worldwide ridesharing network, but plenty has happened since the company began development work in 2015. The rideshare company restarted AV test drives on select public roads in December 2018, nine months after one of its self-driving Volvo development vehicles was involved in a pedestrian fatality in Tempe, Arizona.
“A larger and larger amount of our testing is now being accomplished with simulation. We’re also now concentrating our physical resources more on the test track, or on very specific road tests that can prove out things that we’ve already checked in simulation and on the track,” Stephen Lesh, Head of Hardware Engineering & Vehicle Programs for Uber’s Advanced Technologies Group (ATG), said in an interview with SAE's Autonomous Vehicle Engineering at the recent M:bility Detroit conference.
More than 1,000 engineers and other specialists are working at Uber ATG hubs in Pittsburgh, San Francisco and Detroit, as well as in Toronto for R&D projects. Volvo XC90 SUVs have been Uber’s autonomous-driving test fleet mainstay, but other vehicles are slated to join the development program.
Uber and Toyota announced a deeper AV collaboration in August 2018. Each company’s technologies will be integrated into purpose-built Toyota vehicles with the initial Autono-MaaS (autonomous mobility as a service) fleet based on the Toyota Sienna minivan platform. According to a Toyota Research Institute spokesperson, vehicle deliveries are slated for 2021. Said Lesh, “We plan to work with multiple OEMs now and in the future. Although we’ve announced some collaborations and partnerships, we haven’t announced everything.”
Technology-acquisition options
Uber’s test vehicles are retrofitted with DC-DC converters and other off-the-shelf hardware, but not all of the needed technologies are commercially available—or available from a supplier. “If a technology doesn’t exist, we design it. If a technology partially exists, we partner to develop it,” said Lesh, noting that companies developing the next generation of sensing and compute technologies are among Uber’s supplier partners.
Virtually all development projects at Uber have a tandem focus. “We work on both the hardware and the software as a single engineering team—and we think that’s one of the advantages that Uber has versus companies that are just doing software and then buying a hardware kit,” Lesh said.
Field-of-vision studies for sensors using Computer-Aided Engineering (CAE) analysis is a prime point of investigation. “Compared to designing human-driver sightlines via the seating position and pillar locations, we need autonomous rideshare vehicles to be set up with all the sensors having an optimum field-of-view to the surroundings. That’s why we’re doing a lot of work relating to the lines of sight,” Lesh said.
Every day around the world, Uber drivers complete 15 million rideshare trips. “We want autonomous vehicles to supplement that network, not replace it.,” Lesh said. Today, there aren’t any Uber network trips with AVs. “It’s hard to predict timing, but regulatory actions need to occur and the public needs to trust that the autonomy is verified and that it’s safe. Those factors will drive our network rideshare deployment,” Lesh maintained.
Top Stories
INSIDERRF & Microwave Electronics
FAA to Replace Aging Network of Ground-Based Radars
PodcastsDefense
A New Additive Manufacturing Accelerator for the U.S. Navy in Guam
NewsSoftware
Rewriting the Engineer’s Playbook: What OEMs Must Do to Spin the AI Flywheel
Road ReadyPower
2026 Toyota RAV4 Review: All Hybrid, All the Time
INSIDERDefense
F-22 Pilot Controls Drone With Tablet
INSIDERRF & Microwave Electronics
L3Harris Starts Low Rate Production Of New F-16 Viper Shield
Webcasts
Energy
Hydrogen Engines Are Heating Up for Heavy Duty
Energy
SAE Automotive Podcast: Solid-State Batteries
Power
SAE Automotive Engineering Podcast: Additive Manufacturing
Aerospace
A New Approach to Manufacturing Machine Connectivity for the Air Force
Software
Optimizing Production Processes with the Virtual Twin



