Microsoft research AI project HAMS automates driver's license tests in India

Image Source : Microsoft News

As AI starts to increasingly pervade into every aspect of our lives, we have been hearing about more and more cases of where they are getting implemented to solve real world problems effectively. One such case is Microsoft's research AI Project named HAMS which is short for "Harnessing Automobiles for Safety". This project was initially conceived to monitor drivers and their driving with an aim to improve road safety.

“The main challenge in the traditional driver’s license test is the burden placed on the human evaluators and the resulting subjectivity that a candidate faces. Automation using HAMS technology can not only help relieve evaluators of the burden but also make the process objective and transparent for candidates,” says Venkat Padmanabhan, Deputy Managing Director, Microsoft Research India, who started the HAMS project in 2016.

HAMS utilizes the front and rear camera's of a smartphone mounted on the windscreen in conjunction with other sensors to not only monitor the vehicle's precise trajectory and the road in front but also the driver's gaze. For instance, it checks whether the driver scanned their mirrors before effecting a lane change, and even more rudimentary, whether the person taking the test is the same as the one who registered for it. The comprehensiveness of the test can be judjed by the fact that it also monitors things such as time taken, number of stoppages and number of retries while performing manoeuvres such as parallel parking.

Today, if you take the driver’s license test at the Dehradun RTO, you will be doing so in just the company of a smartphone affixed to your car’s windshield. HAMS, running on the smartphone and on an edge server onsite at the testing track, will do the rest and produce a detailed report shortly after you finish navigating through the test manoeuvres.

“The successful deployment of the HAMS-based driver license testing at the Dehradun RTO is a significant step towards the Transport Department’s goal of providing efficient, world-leading services to the citizens of Uttarakhand. We are proud to be among the pioneers of the application of AI to enhance road safety,” said Shri Shailesh Bagauli, IAS, Secretary, Government of Uttarakhand.

Here is a video of HAMS in action:

  

Although the comprehensiveness of the tests will ensure that we get better and safer driver on the roads but it a distinction has to be made between testing a driver for safety and testing a driver for skill. In the parallel parking example sighted the driver's skill is being tested. The time taken and the number of re-tries would at most cause inconvenience to others rather than hamper their safety. On the other hand strictest scrutiny must be done for things like maintaining lane, braking, lane changing, timing of using indicators, etc. as these are factors related to safety.

Robotic hand made by Elon Musk's OpenAI learns to solve Rubik's Cube

Image Source : OpenAI Blog

Last year we were amazed by the level of dexterity achieved by OpenAI's Dactyl system which was able to learn how to manipulate a cube block to display any commanded side/face.If you missed that article, read about it here.

OpenAI then set themselves a harder task of teaching the robotic hand to solve a Rubik's cube. Quite a daunting task made no easier by the fact that it would use one hand which most humans would find it hard to do. OpenAI harnessed the power of neural networks which are trained entirely in simulation. However, one of the main challenges faced was to make the simulations as realistic as possible because physical factors like friction, elasticity etc. are very hard to model.

The solution they came up with was a new method called Automatic Domain Randomization which endlessly generates progressively more difficult environments for the simulations to solve the Rubik's cube in. This ensures that real world physics gets covered in the spectrum of environments generated and hence bypasses the need to train the simulations on highly accurate environmental models.  

One of the parameters randomized was the size of the Rubik’s Cube. ADR begins with a fixed size of the Rubik’s Cube and gradually increases the randomization range as training progresses. The same technique is applied to all other parameters, such as the mass of the cube, the friction of the robot fingers, and the visual surface materials of the hand. The neural network thus has to learn to solve the Rubik’s Cube under all of those increasingly more difficult conditions.

Here is an uncut version of the robot hand solving the Rubik's cube:

To test the limits of this method, they experimented with a variety of perturbations while the hand is solving the Rubik’s Cube. Not only does this test for the robustness of the control network but also tests the vision network, which is used to estimate the cube’s position and orientation. It was found that the system trained with ADR is surprisingly robust to perturbations. The robot can successfully perform most flips and face rotations under all tested perturbations, though not at peak performance.

The impressive robustness of the robot hand to perturbations can be seen in this video: