Loading video...

Video Failed to Load

Go Home

Presenting autonomous driving in complex, stochastic and adversarial traffic-dynamics, on the roads in #India. Over the course of last year, we enabled #autonomousdriving in conditions and in situations no one in the autonomous driving industry considered was possible, and in a country like India, where the contemporary belief was...

95,416 views • 2 years ago •via X (Twitter)

10 Comments

Jayanth's profile picture
Jayanth2 years ago

When GMaps was introduced in the US in 2005, I thought it would never work in India. It was an incredible achievement that it worked seamlessly. Now, I think autonomous vehicles are impossible for India. It would be an amazing achievement if this is proven to work here. Kudos!

mr.y's profile picture
mr.y2 years ago

What's the timeline to reach Level 5 Autonomous in India?

Sanjeev Sharma's profile picture
Sanjeev Sharma2 years ago

If you look at our demo done on Nov 15, 2017, it was Multi-Agent Reinforcement Learning demo -- this was the time when only Deep Mind was working on RL at a practical scale, that too single agent. That demo enabled autonomous driving end-to-end in our colony environment, and can be considered level-5 capability. We were unfunded at that time, and I was bootstrapping Swaayatt. Similarly, if you look our bidirectional negotiation done on October 2023, you will see it is world's first introduction of this capability (second only to our Nov 15, 2017 demo) -- which again is being scaled for Level-5 autonomous driving. Attaining Level-5 end-to-end, would be like navigating the core city traffic daytime in India -- I think we are 2 years away from it (but we never know -- we will try to pull this off around August 2024 as well) and perhaps by that time we will have raised around $1B to do the necessary research and the infrastructure to support it. The only funds we received thus far was $3M funding in 2021.

Priyanshu Ratnakar's profile picture
Priyanshu Ratnakar2 years ago

hell yeah !

iridium's profile picture
iridium2 years ago

@mp_index bhopal mai autonomous driving ka experiment hua hai.

Niranjan Patil's profile picture
Niranjan Patil2 years ago

@mankuthimma Awesome! May I know what state or central Govt approvals you had to take to run this trial?

longestwavelength's profile picture
longestwavelength2 years ago

@mryalamanchi @protosphinx i think you guys should see this

advait's profile picture
advait2 years ago

I love the tech, although can you show the clips at 1x speed? I understand that you show them at 2x so you can fit more content, although showing these tests in 2x distorts the perception of the viewer as to how it’s actually driving. I’d recommend doing more cuts to fit things in along with some text to explain if you want the viewer to specifically concentrate on something. Love seeing this tech being built here itself 💙

Mithilesh Pamnani's profile picture
Mithilesh Pamnani2 years ago

This is awesome! Keep going!

Catbaba's profile picture
Catbaba2 years ago

Looks like you implemented the whole of Sutton and Barto's book in that car, and then some more :)

Related Videos

Autonomous driving through tight, dynamic, stochastic, and adversarial traffic-dynamics on sub-urban roads in India, as well as through partially unstructured environments. This demos showcases the robustness of our motion planning and decision making algorithmic frameworks in enabling #autonomousdriving through seamlessly through such traffic and environmental scenarios. The vehicle starts from a generic open environment at the temple, where there are no traffic-rules to abide by. It then exits the region and assumes a generic autonomous navigation behaviour, negotiating complex traffic scenes. At various points it can be seen that the other vehicles (bikes, autos, bicyclists, and cars) didn't abide by any traffic-rule and moved in crisscross fashion, presenting adversarial scenarios, challenging our autonomous vehicle at Swaayatt Robots to take care of the collision avoidance. This classical motion planning and decision making algorithmic framework is being further scaled up with deep #reinforcementlearning, which will practically solve the sub-urban traffic-dynamics and environment negotiation for #autonomousvehicles in India and throughout the world as well. This demo was done at the Kankali Kali Mata mandir in the city of Bhopal. This demo was a culmination of our prior works and demos: off-roads, on-roads, bidirectional traffic negotiation in single lane roads, and toll-plaza navigation. We have taken up the arduous task of solving the Level-4 autonomous driving by the end of 2024, globally. #machinelearning #deeplearning

Sanjeev Sharma

186,204 views • 2 years ago

Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning

Sanjeev Sharma

332,875 views • 2 years ago

Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India

Swaayatt Robots

22,200 views • 9 months ago

Autonomous driving through extremely-tight-dynamic environments with complex, stochastic, and adversarial traffic-dynamics, or simply through an absolute chaos, on sub-urban unstructured roads in India. This kind of traffic and environment has never been attempted in the history of #autonomousdriving. There were no traffic-rules to abide by on this road, other than to perform a left-sided avoidance, if the other obstacles follow the same, else the vehicle will have to change its plan in a stochastic manner, in several of the adversarial multi-agent negotiation settings encountered throughout the autonomous navigation. This demos tested our motion planning and decision making framework to its limits, showcasing its robustness in negotiating such traffic-dynamics with ease. This demo was done on mostly a very narrow road, suited mostly for one-way navigation, but as is customary in India, bidirectional traffic is active on such narrow roads. It can be seen throughout navigation that the incoming vehicles didn't allow any gaps for our #AutonomousVehicles, forcing it to negotiate passively-aggressively its own path through the chaos. Furthermore, obstacles overtaking us didn't follow any rules either, and zig-zagged and moved in crisscross fashion, challenging our motion and behaviour planning software, which negotiated all such scenarios with ease. There were only two points where our vehicle came to halt, when two girls on a two-wheeler didn't stop and just kept on navigating, despite our vehicle being closer to the narrow passage and it having the right of way, and despite a bike being parked over there by someone, making it a very challenging scenario both for the humans and for the decision making autonomous agent(s). This demo was done in the Awadhpuri area, on the Durga Mata road. This framework was last shown in relatively much sparser traffic in our Kankali Kali Mata demo last month. It is being scaled further with deep unsupervised and #reinforcementlearning , and in the coming weeks, it will play a critical role in our endeavour to solving the Level-4 autonomy problem by the end of the year. This kind of traffic negotiation has never been attempted by any autonomous driving company ever. While a 90-degree turn is usually discussed as a corner case in the West, our autonomous vehicle negotiated a blind 90-degree corner, with traffic, with ease. #deeplearning #MachineLearning Swaayatt Robots

Sanjeev Sharma

125,218 views • 2 years ago

Autonomous driving and obstacles avoidance at drift speeds, challenging the limits of what is possible! In this demo, our vehicle can be seen performing #autonomousdriving at very high speeds, causing it to both skid and drift at turns, while also avoiding obstacles. At such speeds, given the inherent dynamics of the vehicle platform used, it is very easy for the vehicle to topple. The #reinforcementlearning based motion planning and decision making framework that is being demoed here is tasked with ensuring obstacles avoidance without compromising on the speed, to an extent possible, and to drive the vehicle as fast as possible. This is evident towards the end of the video, where it can be seen that our vehicle avoided static obstacles while drifting.This demonstrates the level of sophistication and agility in our framework to ensure proper control of the #autonomousvehicles at high-speeds. The use cases are many; to begin with, our generic off-roads autonomous driving research focuses on enabling autonomous navigation in previously unknown and unseen environments, while ensuring mathematical completeness guarantees. Such agility can also help on-road autonomous vehicles to deal with unforeseeable corner cases or sudden appearance of obstacles in its tracks, at high-speeds. Our underlying research at Swaayatt Robots is still far from over, and over the next 3-4 months, we will be demonstrating abstract representation being learned by our multi-RL agents based framework (under progress) to ensure computation of the cost of the terrain without any labelled data, where multiple agents learn to control / regulate different aspects of autonomous navigation, to ensure safe and robust navigation, both on- and off-roads. All the people on the ground, who participated in the demo, were trained safety professionals. #deeplearning #MachineLearning #Robotics

Sanjeev Sharma

11,181 views • 1 year ago

Today we're announcing our new office and autonomous fleet in Japan! 🙌 This has been a year in the making — here's the story behind Wayve's journey to Japan 🇯🇵 Just over one year ago I was looking over the Tokyo skyline as we were completing our Series C fundraise, wondering how our AI would navigate the narrow winding streets. At that point it was a dream, but in the last year, it has become reality. The right team at the right time: 6 months ago we made our first hire in Japan. I'm grateful for Masa Kameyama taking a bet on Wayve and for his partnership leading our presence in Japan (we have a lot of great people around Wayve called Masa 😄). I'm excited about the opportunity to welcome world-class and diverse experience to Wayve's team. We're hiring and building an awesome team in Japan! The right product at the right time: for a long time I've been impressed by Japanese automaker's scale and leadership in safety and lean manufacturing. Now I'm witnessing bold and brave decisions from automotive executives to lean into AV2.0. There is an opportunity for Japanese automakers to leap-frog other markets to be the first to bring AV2.0 to global scale. We are delighted to be partnering with Nissan Motor to make this a reality in 2027 and to be working with partners like Uber, タクシーアプリ S.RIDE(エスライド) 公式アカウント and SoftBank. Fast forward to yesterday, I landed in Tokyo and was treated by being picked up by one of our autonomous vehicles! Today, I’ve just got off stage at our press conference, launching our brand new Testing and Development Centre in Yokohama and autonomous vehicle fleet in Japan. Our fleet of autonomous vehicles are now testing around the world in UK 🇬🇧, Germany 🇩🇪, Canada 🇨🇦, US 🇺🇸 and now Japan 🇯🇵. This is an important proof point showing Wayve is capable of being a global company and delivering the benefits of Embodied AI to consumers around the world.

Alex Kendall

41,277 views • 1 year ago