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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...

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Sanjeev Sharma

6,014 subscribers

11,181 просмотров • 1 год назад •via X (Twitter)

ОбразованиеНаука и технологии#autonomousdriving
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Комментарии: 10

Фото профиля Rahul Pandey
Rahul Pandey1 год назад

Nice

Фото профиля next person
next person1 год назад

Absolutely fantastic? You taking funding?

Фото профиля Sanjeev Sharma
Sanjeev Sharma1 год назад

Thank you so much 🙏 Yes we are actively raising funds :)

Фото профиля pkc
pkc1 год назад

That rough patch and evening dark mode with other vehicle and rifting seems amazing 👌

Фото профиля Subhash Sadhu
Subhash Sadhu1 год назад

Great results @sanjeevs_iitr ji. What is your target market/application?

Фото профиля Sanjeev Sharma
Sanjeev Sharma1 год назад

Thank you so much 🙏We are solving generalized autonomous driving problem. The off roads autonomous driving stack in particular is targeted towards military and other off road applications.

Фото профиля Vivek
Vivek1 год назад

Incredible! Autonomous driving at drift speeds with obstacle avoidance—pushing the boundaries of what's possible. I'm a big fan of this work! 🚀

Фото профиля ©BijapurBoy
©BijapurBoy1 год назад

Congrats.wiah you more success even more early. The Indian FSD. Mean while liked your choice of bolero and not a maruti...@anandmahindra @mahindraesuvs checknthis.

Фото профиля Abhi
Abhi1 год назад

well, are you making any video or show to show us your work something like elon did in spacex tour, means example, and can you tell timeline for final completion or any future update of funding or research

Фото профиля yolo
yolo1 год назад

good job.

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68,149 просмотров • 4 месяцев назад

In this demo we extend our prior work on obstacles avoidance at aggressive speeds, showcasing our Thar based autonomous vehicle navigating at near drift speeds, progressing towards our endeavour of Level-5 autonomy. Our autonomous vehicle at Swaayatt Robots was tasked with avoidance of traffic cones on the road, placed in a zig-zag fashion, at aggressive speeds. The location of the marked cones was not known to the planner beforehand. The #autonomousdriving task, i.e., motion planning (time parametrized trajectory computation) and decision making, was made even more challenging by restricting the AI agents to not act on obstacles unless they are within 24m radius. Level-5 #autonomousvehicles should be able to react quickly to overtake, or to avoid, any sudden unforeseeable obstacle or pedestrian on the road to avoid fatalities -- a capability demonstrated by our novel motion planning and decision making algorithmic framework over here. Our previous demo showcased our Bolero based platform consistently keeping speeds beyond 45 KMPH for most part, slowing down to only 39 KMPH at one point. Given Thar has lesser body roll, our framework successfully kept speeds well above 47 KMPH (even at the points of avoidance of obstacles), with speeds reaching as high as 55 KMPH. A typical human driver would feel uncomfortable at speeds beyond 40 KMPH in such as scenario. The entire algorithmic framework with 5 classical (one #reinforcementlearning-) agents , runs at 800+ Hz on a regular i7 processor, single thread. This algorithmic framework is being further scaled up with end-to-end deep reinforcement learning, and will be showcased in the month of March. #deeplearning #machinelearning #motionplanning
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12,811 просмотров • 1 год назад

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
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22,200 просмотров • 9 месяцев назад

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Enabling autonomous vehicles perceive their environment using only off-the-shelf cameras has been a long term research objective at Swaayatt Robots. This demo highlights the capabilities of our on-road perception system which is able to detect obstacles, road boundaries, lane markers in images, as well as compute depth of the complex scenes in the environment. The output shown in this video is end-to-end raw output from our deep learning system, without any post processing. The current system, with joint computation of obstacles, lane/road boundaries, and depth, works at 30 FPS on an embedded GPU in our autonomous vehicle, and can achieve higher FPS with further optimization -- which is currently a research in progress. This system is being scaled up for both the day and night operations, and we will showcase its strength towards enabling autonomous driving on a mountainous environment with unpaved roads, in the absence of any delimiters. #deeplearning #autonomousdriving #autonomousvehicles #machinelearning

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18,478 просмотров • 1 год назад

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
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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 просмотров • 2 лет назад

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
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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 просмотров • 2 лет назад

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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 that #autonomousvehicles are an impossibility. With the closure of 2023, we present autonomous driving at a very large scale, in the city of Bhopal in India. In this demo our autonomous vehicle at Swaayatt Robots can be seen negotiating the surrounding traffic with ease, where it had to deal with their stochastic and adversarial driving patterns, like suddenly switching lanes and appearing all of a sudden in our vehicle's current driving lane, without adherence to the traffic rules. This demo was a culmination of the cutting-edge research we have been doing, and the technologies we have been developing, over the years, which we showcased throughout in our demos in 2023 -- campus autonomous driving (February), off-roads autonomous driving (April and September), tight-stochastic traffic negotiation (August and September), bidirectional traffic negotiation on a single lane road (October), large-scale city level demo (November), and Toll-Plaza negotiation (December). In 2024, we will scale our technology commercially, and bring it to North America and Europe, to topple this trillion dollar industry, and will also scale our technology throughout India. Wishing everyone a very Happy New Year! #deeplearning #reinforcementlearning #MachineLearning Elon Musk PMO India Narendra Modi Nitin Gadkari DARPA DRDO

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95,416 просмотров • 2 лет назад

QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.
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QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.

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12,654 просмотров • 6 месяцев назад

Hyundai Motor's self-driving group '42Dot' has released an 'End-to-End' self-driving video. 42Dot introduces images of the Atria AI experiment drive, adding the following explanation. - Atria AI is an end-to-end driving-based autonomous driving technology developed independently by 42dot that recognizes the surroundings and drives with only eight cameras on the vehicle without relying on an HD map. - It also uses the 400 TOPS class NPU to implement Navigation on Autopilot (NOA) capabilities on highways and cities, and is a prime example of the direction of software-defined autonomous driving technology. What do you think?
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Hyundai Motor's self-driving group '42Dot' has released an 'End-to-End' self-driving video. 42Dot introduces images of the Atria AI experiment drive, adding the following explanation. - Atria AI is an end-to-end driving-based autonomous driving technology developed independently by 42dot that recognizes the surroundings and drives with only eight cameras on the vehicle without relying on an HD map. - It also uses the 400 TOPS class NPU to implement Navigation on Autopilot (NOA) capabilities on highways and cities, and is a prime example of the direction of software-defined autonomous driving technology. What do you think?

Tsla Chan

90,628 просмотров • 6 месяцев назад

The countdown is on, Austin 🤠🤖🚘! Waymo autonomous rides are arriving soon — only on Uber. Starting today, Austin customers can open the Uber app and join the Interest List to get updates from Uber and increase their chances of being matched with a Waymo autonomous vehicle at launch. Tap our link in bio to learn more.
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The countdown is on, Austin 🤠🤖🚘! Waymo autonomous rides are arriving soon — only on Uber. Starting today, Austin customers can open the Uber app and join the Interest List to get updates from Uber and increase their chances of being matched with a Waymo autonomous vehicle at launch. Tap our link in bio to learn more.

Uber

53,280 просмотров • 1 год назад

As our schools reopen for the new term, I urge every Kenyan to join hands in ensuring the safety of our children on the roads. With thousands of students returning to school across the country, our roads will experience a surge in traffic. To motorists: -Slow down and be alert when approaching school zones and pedestrian crossings; children may be walking and crossing unexpectedly. -Obey speed limits, traffic signs, and signals at all times. -Ensure your vehicle is roadworthy; brakes, lights, tyres, and mirrors must be in good condition. -Do not overload vehicles carrying children. To parents and guardians: -Choose safe and compliant transport for your children. -Teach children the basics of road safety; looking both ways before crossing and using designated crossings. The safety of our children is everyone’s responsibility. Let us uphold our traffic laws, be mindful of one another, and ensure that every learner arrives safely at school and returns home at the end of the day. Report reckless behaviour to the authorities NTSA KENYA, and National Police Service-Kenya, your vigilance can save lives. Stay safe, stay alert, and let us protect our future generation on the roads.
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As our schools reopen for the new term, I urge every Kenyan to join hands in ensuring the safety of our children on the roads. With thousands of students returning to school across the country, our roads will experience a surge in traffic. To motorists: -Slow down and be alert when approaching school zones and pedestrian crossings; children may be walking and crossing unexpectedly. -Obey speed limits, traffic signs, and signals at all times. -Ensure your vehicle is roadworthy; brakes, lights, tyres, and mirrors must be in good condition. -Do not overload vehicles carrying children. To parents and guardians: -Choose safe and compliant transport for your children. -Teach children the basics of road safety; looking both ways before crossing and using designated crossings. The safety of our children is everyone’s responsibility. Let us uphold our traffic laws, be mindful of one another, and ensure that every learner arrives safely at school and returns home at the end of the day. Report reckless behaviour to the authorities NTSA KENYA, and National Police Service-Kenya, your vigilance can save lives. Stay safe, stay alert, and let us protect our future generation on the roads.

Davis Chirchir, EGH

24,595 просмотров • 5 месяцев назад

RECKLESS! On Monday, December 1, around 12:30 a.m., AIR5 spotted a reckless driver on US 95 at Charleston. The light-colored sedan was traveling at speeds between 120 and 140 miles per hour, weaving dangerously close to cars and passing on the shoulder. The vehicle was seen sparking at some points while Air 5 tried to keep up with the dangerous speeds. As you can see from this helicopter video, this could have ended in tragedy. One of our patrol officers spotted the vehicle and pulled it over on the southbound I-15 ramp from US 95. Behind the wheel was an 18-year-old who showed signs of impairment. The car also had a 16, 17, and another 18-year-old inside. Dangerous, deadly, and downright dumb. The driver was arrested, and the car was impounded. We are cracking down on dangerous driving, from the air and on the ground. So far, 148 people killed on roads within our jurisdiction in 2025. Event: LLV251200000100
1:37

RECKLESS! On Monday, December 1, around 12:30 a.m., AIR5 spotted a reckless driver on US 95 at Charleston. The light-colored sedan was traveling at speeds between 120 and 140 miles per hour, weaving dangerously close to cars and passing on the shoulder. The vehicle was seen sparking at some points while Air 5 tried to keep up with the dangerous speeds. As you can see from this helicopter video, this could have ended in tragedy. One of our patrol officers spotted the vehicle and pulled it over on the southbound I-15 ramp from US 95. Behind the wheel was an 18-year-old who showed signs of impairment. The car also had a 16, 17, and another 18-year-old inside. Dangerous, deadly, and downright dumb. The driver was arrested, and the car was impounded. We are cracking down on dangerous driving, from the air and on the ground. So far, 148 people killed on roads within our jurisdiction in 2025. Event: LLV251200000100

LVMPD

32,250 просмотров • 6 месяцев назад

U.S. Transportation Secretary Sean Duffy at the first autonomous vehicle safety forum today: “A lot of our top innovators from Waymo, Zoox and Tesla are here, and we're going to have a day long conversation; If you have an autonomous vehicle, do you need a steering wheel? We have to rethink some of our requirements, especially for autonomous vehicles, which will allow them to bring down price, maintain the safety structure, but move forward and be competitive with the rest of the world."
1:52

U.S. Transportation Secretary Sean Duffy at the first autonomous vehicle safety forum today: “A lot of our top innovators from Waymo, Zoox and Tesla are here, and we're going to have a day long conversation; If you have an autonomous vehicle, do you need a steering wheel? We have to rethink some of our requirements, especially for autonomous vehicles, which will allow them to bring down price, maintain the safety structure, but move forward and be competitive with the rest of the world."

Sawyer Merritt

135,379 просмотров • 3 месяцев назад

#WATCH | Delhi CM Rekha Gupta says, "From November 1, 2025, any vehicle that comes to Delhi will have to be a BS6, CNG, or EV commercial vehicle. Together, we can deal with the big problem of pollution in Delhi...We will install ANPR- Automatic Number Plate Registration Recognition cameras at all the entry points of Delhi, which will be able to identify end-of-life vehicles and as soon as the vehicle comes in the range of these cameras, it will be known that this vehicle is end-of-life vehicle and is spreading pollution, so they will be traced and stopped immediately. These cameras will also be installed at all petrol pumps and we will be able to detect end-of-life vehicles..."
2:13

#WATCH | Delhi CM Rekha Gupta says, "From November 1, 2025, any vehicle that comes to Delhi will have to be a BS6, CNG, or EV commercial vehicle. Together, we can deal with the big problem of pollution in Delhi...We will install ANPR- Automatic Number Plate Registration Recognition cameras at all the entry points of Delhi, which will be able to identify end-of-life vehicles and as soon as the vehicle comes in the range of these cameras, it will be known that this vehicle is end-of-life vehicle and is spreading pollution, so they will be traced and stopped immediately. These cameras will also be installed at all petrol pumps and we will be able to detect end-of-life vehicles..."

ANI

173,694 просмотров • 1 год назад

NEWS: Hyundai's self-driving unit 42dot has released a new video showing off its end-to-end autonomous driving software on the roads of South Korea, and parking. "It drives using only eight cameras mounted on the vehicle, without relying on an HD maps. It utilizes a 400 TOPS-class NPU to implement the NOA (Navigation on Autopilot) function on highways and in urban areas."
1:48

NEWS: Hyundai's self-driving unit 42dot has released a new video showing off its end-to-end autonomous driving software on the roads of South Korea, and parking. "It drives using only eight cameras mounted on the vehicle, without relying on an HD maps. It utilizes a 400 TOPS-class NPU to implement the NOA (Navigation on Autopilot) function on highways and in urban areas."

Sawyer Merritt

506,130 просмотров • 6 месяцев назад

And this is what real scaling of an autonomous vehicle network looks like on the roads!
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And this is what real scaling of an autonomous vehicle network looks like on the roads!

🇩🇪China EV, Engineering & Life🇨🇳

13,543 просмотров • 3 месяцев назад

One of the best ways to reinforce NASA’s mission-first culture is to free the best and brightest from needless bureaucracy and obstacles that slow progress, so they can focus on science and discovery as quickly and affordably as possible without compromising safety. My advisors (or “team”) and I have established inboxes we will monitor ourselves and encouraged their use to bypass unnecessary filters. My roadshow to every NASA center, which kicked off Friday, gives me the opportunity to spend time with those in the trenches and listen closely to our industry and international partners. I will do everything I can to remove blockers, empower the workforce, and ensure sustained American leadership in the high ground of space.
0:53

One of the best ways to reinforce NASA’s mission-first culture is to free the best and brightest from needless bureaucracy and obstacles that slow progress, so they can focus on science and discovery as quickly and affordably as possible without compromising safety. My advisors (or “team”) and I have established inboxes we will monitor ourselves and encouraged their use to bypass unnecessary filters. My roadshow to every NASA center, which kicked off Friday, gives me the opportunity to spend time with those in the trenches and listen closely to our industry and international partners. I will do everything I can to remove blockers, empower the workforce, and ensure sustained American leadership in the high ground of space.

NASA Administrator Jared Isaacman

45,022 просмотров • 5 месяцев назад