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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 #motionplanningshow more

Sanjeev Sharma

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12,811 görüntüleme • 1 yıl önce •via X (Twitter)

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Akshay Charegaonkar 🇮🇳 profil fotoğrafı

Akshay Charegaonkar 🇮🇳1 yıl önce

@swaayatt This experimental autonomous car is already the safest Thar driver in India 😅

Sanjeev Sharma profil fotoğrafı

Sanjeev Sharma1 yıl önce

@swaayatt 🙏

Globant profil fotoğrafı

Globant1 yıl önce

🚀 Agentic AI Systems are changing what AI can do by having the power to act independently. Unlike traditional AI, which needs constant human supervision, these systems can operate more autonomously. Learn how this shift is leading to smarter solutions that can transform industries.➡️ #TechTrends2025

clewyclawy profil fotoğrafı

clewyclawy1 yıl önce

@swaayatt Please test this scenario- Cruising at 100 kph on fastest lane of a smooth multi-lane highway and a straight road with huge bushes growing on the median separating the two carriageways. Now a 100kg bull invisible behind one such bush darts across the road 10 metres from the car

pranav13 profil fotoğrafı

pranav131 yıl önce

@swaayatt Great going, karmayogi

Natsec enjoyer profil fotoğrafı

Natsec enjoyer1 yıl önce

@swaayatt Hi, I see most of the autonomous driving tests which you do are on this particular road stretch only ( by the looks of it). Won't the AI become accustomed of driving the same road again and again? You should try different terrains and different roads. Pls don't get me wrong pls🙏

Ram profil fotoğrafı

Ram1 yıl önce

@swaayatt @volklub Do you believe India can have driverless cars on Indian roads?

Hashirama_Senju profil fotoğrafı

Hashirama_Senju1 yıl önce

@swaayatt Go swaaayatt 🥰🥰🥰🥰💪💪💪💪💪

Infinite profil fotoğrafı

Infinite1 yıl önce

@swaayatt Try on porche on tata new avinya or new mahindra ev, i know budget issue, but porche will be nice troll

vijaya dchauhan profil fotoğrafı

vijaya dchauhan1 yıl önce

@swaayatt Looking forward 👍🏻

鑫椤智驾 profil fotoğrafı

鑫椤智驾1 yıl önce

@swaayatt The research on autonomous driving by Indian scientists is impressive, especially considering that traffic conditions in India are even more complex than those in China.

Benzer Videolar

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

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 görüntüleme • 1 yıl önce

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

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 görüntüleme • 2 yıl önce

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

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 görüntüleme • 2 yıl önce

Autonomous dodging off-roads without any human presence. This is a demonstration of pushing the limits of what is possible in off-road #autonomousdriving. In this demo, we performed autonomous dodging without any human presence in our autonomous vehicle, that too off-roads. In this mode, collision avoidance against aggressive-stochastic-adversarial traffic is the sole responsibility of #AutonomousVehicles. Our autonomous vehicle, Deep Xplorer, was tasked with navigating an off-road trail, avoiding both the static and dynamic obstacles. Furthermore, team members on bikes introduced random cross-traffic interactions by cutting its path at random, creating highly adversarial and stochastic traffic scenarios. To increase the stakes even further, I personally drove our other autonomous vehicle (Xplorer), cutting the path of Deep Xplorer at random --leaving the task of negotiation, motion planning, and decision making, to ensure collision avoidance and safety of the vehicles and the environment as a sole responsibility of our autonomous driving stack in Deep Xplorer. This is the first time in the history of Swaayatt Robots that we performed autonomous dodging without human presence in our autonomous vehicle, pushing the limits of what is possible in autonomous driving landscape. #reinforcementlearning #deeplearning

Autonomous dodging off-roads without any human presence. This is a demonstration of pushing the limits of what is possible in off-road #autonomousdriving. In this demo, we performed autonomous dodging without any human presence in our autonomous vehicle, that too off-roads. In this mode, collision avoidance against aggressive-stochastic-adversarial traffic is the sole responsibility of #AutonomousVehicles. Our autonomous vehicle, Deep Xplorer, was tasked with navigating an off-road trail, avoiding both the static and dynamic obstacles. Furthermore, team members on bikes introduced random cross-traffic interactions by cutting its path at random, creating highly adversarial and stochastic traffic scenarios. To increase the stakes even further, I personally drove our other autonomous vehicle (Xplorer), cutting the path of Deep Xplorer at random --leaving the task of negotiation, motion planning, and decision making, to ensure collision avoidance and safety of the vehicles and the environment as a sole responsibility of our autonomous driving stack in Deep Xplorer. This is the first time in the history of Swaayatt Robots that we performed autonomous dodging without human presence in our autonomous vehicle, pushing the limits of what is possible in autonomous driving landscape. #reinforcementlearning #deeplearning

Sanjeev Sharma

68,153 görüntüleme • 4 ay önce

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

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 görüntüleme • 9 ay önce

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

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

125,227 görüntüleme • 2 yıl önce

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

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

Sanjeev Sharma

18,478 görüntüleme • 1 yıl önce

New Pamban Bridge: getting closer to commissioning! A successful OMS-engine run showcased the precision and strength of the Bridge, reaching speeds of 121 kmph on the Mandapam-Rameswaram section in Tamil Nadu and 80 kmph on the bridge itself.

New Pamban Bridge: getting closer to commissioning! A successful OMS-engine run showcased the precision and strength of the Bridge, reaching speeds of 121 kmph on the Mandapam-Rameswaram section in Tamil Nadu and 80 kmph on the bridge itself.

Ministry of Railways

72,089 görüntüleme • 1 yıl önce

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

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 görüntüleme • 6 ay önce

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

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

Sanjeev Sharma

95,416 görüntüleme • 2 yıl önce

Hyundai Alcazar’s suspension is clearly set up keeping bad Indian roads in mind. What surprised me is how well it stays balanced when the roads are good too. I haven’t driven beyond 110 kmph, so won’t comment on higher speeds. But in the 0–110 kmph range, which is what most people actually drive at in India, the car feels well engineered and suited to our conditions. I do need to mention that I personally would love to test this car with superior set of Michelin, Bridgestone or Yokohama tyres. Right now it has JKTyre. That surely will enhance the grip & braking. (Using it as a primary car for the last week, car provided by Hyundai India)

Hyundai Alcazar’s suspension is clearly set up keeping bad Indian roads in mind. What surprised me is how well it stays balanced when the roads are good too. I haven’t driven beyond 110 kmph, so won’t comment on higher speeds. But in the 0–110 kmph range, which is what most people actually drive at in India, the car feels well engineered and suited to our conditions. I do need to mention that I personally would love to test this car with superior set of Michelin, Bridgestone or Yokohama tyres. Right now it has JKTyre. That surely will enhance the grip & braking. (Using it as a primary car for the last week, car provided by Hyundai India)

Sunderdeep - Volklub

70,778 görüntüleme • 6 ay önce

Despite the vagaries of nature, our workforce stays dedicated. A track maintainer on Pamban carries out his routine checks even as wind speeds cross 68 kmph — commitment that keeps our railways safe.🚆🌊 #SouthernRailway #IndianRailways #PambanBridge #RailwaySafety #Keyman

Despite the vagaries of nature, our workforce stays dedicated. A track maintainer on Pamban carries out his routine checks even as wind speeds cross 68 kmph — commitment that keeps our railways safe.🚆🌊 #SouthernRailway #IndianRailways #PambanBridge #RailwaySafety #Keyman

Southern Railway

30,387 görüntüleme • 6 ay önce

On July 17, 2024, a suspect fled from an attempted traffic stop on US 60 eastbound in Mesa at speeds of over 90 mph. Watch the video to see our trooper deploy the Grappler to force the suspect’s vehicle to a safe stop. 🚔 #AZTroopers #CourteousVigilance

On July 17, 2024, a suspect fled from an attempted traffic stop on US 60 eastbound in Mesa at speeds of over 90 mph. Watch the video to see our trooper deploy the Grappler to force the suspect’s vehicle to a safe stop. 🚔 #AZTroopers #CourteousVigilance

Dept. of Public Safety

37,593 görüntüleme • 1 yıl önce

Heroic Action on Hwy 36: On May 25, 2025, at 5:10 AM, Sgt. McKim stopped a driver who was traveling on the wrong side of Highway 36. The driver reached speeds of up to 110 mph at times, and several cars had to swerve to avoid a head-on collision. Thanks to her quick response, no one was hurt. The driver was arrested without incident and now faces charges including DUI, drug possession, and multiple traffic violations. Well done, Sgt. McKim! If you see a car driving on the wrong side of the road, pull over, stop at a safe location, and call 911 immediately. We’d also like to thank our partners at the Boulder Police Department for their assistance on this call. As in every criminal case, these charges are merely an accusation, and the defendant is presumed innocent unless or until proven guilty.

Heroic Action on Hwy 36: On May 25, 2025, at 5:10 AM, Sgt. McKim stopped a driver who was traveling on the wrong side of Highway 36. The driver reached speeds of up to 110 mph at times, and several cars had to swerve to avoid a head-on collision. Thanks to her quick response, no one was hurt. The driver was arrested without incident and now faces charges including DUI, drug possession, and multiple traffic violations. Well done, Sgt. McKim! If you see a car driving on the wrong side of the road, pull over, stop at a safe location, and call 911 immediately. We’d also like to thank our partners at the Boulder Police Department for their assistance on this call. As in every criminal case, these charges are merely an accusation, and the defendant is presumed innocent unless or until proven guilty.

Boulder County Sheriff’s Office

19,485 görüntüleme • 11 ay önce

Insane protesters in LA moved traffic cones further in on the freeway during the anti-ICE riots in order to try to mess with the traffic. Cars zoomed by at high speeds. These girls are lucky things didn’t get bad. The guy filming yelled at them.

Insane protesters in LA moved traffic cones further in on the freeway during the anti-ICE riots in order to try to mess with the traffic. Cars zoomed by at high speeds. These girls are lucky things didn’t get bad. The guy filming yelled at them.

Paul A. Szypula 🇺🇸

807,632 görüntüleme • 1 yıl önce

Another reproducible critical issue in FSD (China): when driving at relatively low speeds on public roads, FSD fails to avoid obstacles hanging in the air, even when they are very large. Tesla AI

Another reproducible critical issue in FSD (China): when driving at relatively low speeds on public roads, FSD fails to avoid obstacles hanging in the air, even when they are very large. Tesla AI

Raines

11,877 görüntüleme • 1 yıl önce

Here’s a video of a German machine which is supposed to be the gold standard in this field and the performance benchmark for everyone. Just FYI, this german company is over 130 years old. And this machine would be a minimum 10-12 times the cost of our machine. Their speeds seem to be very similar to our machine, if not lower. Please don’t comment on the aesthetics, we’re well aware. We’re not anywhere near the scale at which this company operates and has resources. We, on the other hand had designed, developed, manufactured and tested our machine from scratch with a team of three people (including myself).

Here’s a video of a German machine which is supposed to be the gold standard in this field and the performance benchmark for everyone. Just FYI, this german company is over 130 years old. And this machine would be a minimum 10-12 times the cost of our machine. Their speeds seem to be very similar to our machine, if not lower. Please don’t comment on the aesthetics, we’re well aware. We’re not anywhere near the scale at which this company operates and has resources. We, on the other hand had designed, developed, manufactured and tested our machine from scratch with a team of three people (including myself).

Vishakh Ranotra

716,995 görüntüleme • 5 ay önce

Capybaras are not only the largest rodents in the world, but they can also run underwater at speeds of up to 5 km/h

Capybaras are not only the largest rodents in the world, but they can also run underwater at speeds of up to 5 km/h

internet hall of fame

165,941 görüntüleme • 7 ay önce