Robohub.org
 

Stanford’s self-driving Delorean goes drifting for Back to the Future Day


by
21 October 2015



share this:
delorean

Last night, I attended Stanford’s unveiling of their newest research vehicle for self-driving. An old Delorean has been heavily modified in order to perform drifting experiments – where you let the rear wheels skid freely.

Stanford managed to get Jamie Hyneman of Mythbusters to host the event so there was a good crowd. He asked “Why a Delorean?” What they should have said was:

“The way I see it, if you’re going to build a self-driving drifting car, why not do it with some style?”

But, instead, they got into the technical reasons for choosing a Delorean.

They called the car Marty and it was launched the day before “Back to the Future Day” — Oct 21, 2015, the day in the second movie where Marty travels into the future.

But back to the present. This car, with rear wheel drive and central engine mount, is not a great car to drive. The engineers have removed the engine and replaced it with dual electric motors from Renovo, creating a car able to drive the two rear wheels independently. This means the software is able to spin the wheels at different rates, and do things that no human driver could ever do, including special types of drifting. The car is already able to turn tighter doughnuts (circles) than a human could.

Normally, drifting is a bad idea. It means a loss of control and a loss of power – the connection of the tires and the road is the sole tool you have to drive and control the car. You would only give it up if you absolutely had to. Perhaps the research will show that there are times where you might want to.

Drifting is usually done for show — it will rarely help you in a race — but Stanford’s team wants to discover whether the robot’s ability to do inhuman driving might offer more “outs” in a dangerous situation, like trying to avoid a collision. A car might twist its wheels (perhaps some day all of its wheels) and spin them at different speeds to enable it to take a path which could avoid an accident.

In effect, it’s like making a vehicle that can drive like a Hollywood stunt car. In movies, stunt drivers often make fairly improbable and impossible moves to avoid accidents. A classic Hollywood scene involves a car titling two wheels to get through a tiny gap. The Stanford team did not propose this, and it’s a pretty hard thing to do, but it’s one way to envisage the general idea.

Up to now, research on accident avoidance has been fairly low-key. After all, the main task is to be able to drive safely in the lane you are supposed to be in. But eventually, teams will focus on what to do when things go wrong. For now, though, the priority is to make sure things don’t go wrong. Someday, they may even focus on the infamous trolley problem.

Generally, drift or not, robots should become very good at avoiding accidents. They will have detailed knowledge of the physics of their tires, they will calculate without panic and will be able to drive with full confidence, missing obstacles by very thin margins while staying safe. A human can’t navigate a space only a few inches wider than the car with confidence, but a robot could. A robot will always use the optimal combination of steering and braking, which humans need a lot of training to achieve. Your tires can give you braking force or steering force, but you must reduce one to get more of the other, so often the best strategy is to brake first and then steer, though the human instinct is to do both.

Stanford’s car is not super autonomous. It is meant to do test algorithms in private open spaces. So it won’t be avoiding obstacles or plotting lanes on a highway, it will be testing how a computer can get the most use from the car’s tires.

This article originally appeared on robocars.com.



tags: , , ,


Brad Templeton, Robocars.com is an EFF board member, Singularity U faculty, a self-driving car consultant, and entrepreneur.
Brad Templeton, Robocars.com is an EFF board member, Singularity U faculty, a self-driving car consultant, and entrepreneur.





Related posts :



AI-powered robots help tackle Europe’s growing e-waste problem

  12 May 2025
EU-funded researchers have developed adaptable robots that could transform the way we recycle electronic waste, benefiting both the environment and the economy.

Robot Talk Episode 120 – Evolving robots to explore other planets, with Emma Hart

  09 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Emma Hart from Edinburgh Napier University about algorithms that 'evolve' better robot designs and control systems.

Robot Talk Episode 119 – Robotics for small manufacturers, with Will Kinghorn

  02 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Will Kinghorn from Made Smarter about how to increase adoption of new tech by small manufacturers.

Multi-agent path finding in continuous environments

  01 May 2025
How can a group of agents minimise their journey length whilst avoiding collisions?

Interview with Yuki Mitsufuji: Improving AI image generation

  29 Apr 2025
Find out about two pieces of research tackling different aspects of image generation.

Robot Talk Episode 118 – Soft robotics and electronic skin, with Miranda Lowther

  25 Apr 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Miranda Lowther from the University of Bristol about soft, sensitive electronic skin for prosthetic limbs.

Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.

Robot Talk Episode 117 – Robots in orbit, with Jeremy Hadall

  11 Apr 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Jeremy Hadall from the Satellite Applications Catapult about robotic systems for in-orbit servicing, assembly, and manufacturing.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence