On- and Off-Roads Autonomous Driving in India with Tight Obstacles Avoidance
Ability to negotiate tight-obstacles on any kind of roads is one of the key abilities that autonomous vehicles should demonstrate to be able to be scaled sustainably, and to achieve safety without compromising on the energy consumption.
On September 21st we did an off-roads #autonomousdriving demo "Driving where no Autonomous Vehicle has driven before!" where our vehicle traversed the kind of terrain in a manner that has never been done before by any autonomous driving startup.
On November 15, 2017 we did a very tight dynamic obstacles avoidance demo with the environments and obstacles being adversarial-stochastic-complex-unstructured in nature, using our multi reinforcement learning agents based framework that Sanjeev Sharma, founder and CEO of Swaayatt, was working on that that time -- which was world's first demo of end-to-end deep reinforcement learning applied to real-world autonomous vehicles, that too for such kind of navigation tasks.
Over here in this demo, again one of the laps from Sep 21st demo, we show a custom designed motion planning and decision making algorithmic framework, that is able to negotiate tight passages, without stopping the vehicle completely. This work is further being scaled with unsupervised learning.
At around 28 seconds the vehicle encounters a situation where a car was parked on the left, and a biker was approaching from the other end on the right, leaving not enough gap for the vehicle to pass with a safe enough margin. Usually in such situations, typically autonomous vehicles in the West, would come to a complete halt on the legal driving side, and wait for the other obstacles to pass -- this can also create a dead-lock situation, like what is happening in SF with Cruise, Zoox and Waymo autonomous vehicles, whose software is not properly designed and makes lot of assumptions. Often, the planning software in such vehicles designed by those companies is running at such a low frequency (less than 10 Hz) that is just not safe at all. This gives rise to another problem to solve -- like predicting the future of other obstacles, etc, which again is a dead-end.
Earlier this year Crusie showcased avoiding dense traffic in SF, where it could be easily seen stopping for every possible obstacle imaginable.
Here, in our demo, it can be seen that the vehicle never comes to a complete halt and tries to, and successfully does, find a passage through the obstacles, on a single lane road.
We are scaling this algorithmic framework via inverse unsupervised learning, and very soon its performance will match that of our Nov 15, 2017 demo. The difference being that this framework will be a non-holistic decision making framework, whereas the former was a holistic (every control command was computed by multi RL agents end-to-end). Around January we will showcase this framework converging to a near holistic framework , i.e., when activated by the vehicles' probabilistic decision making framework, it will compute actions without taking input from perception software.