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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...

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Sanjeev Sharma

6,516 subscribers

125,218 次观看 • 2 年前 •via X (Twitter)

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10 条评论

Qui Gon Jinn ✨⚡ 的头像
Qui Gon Jinn ✨⚡2 年前

Appreciate the work but it doesn't seem like those other people in traffic had knowledge of what you were doing and I doubt they at least I wouldn't appreciate being a unwitting participant in your test drive. What if your system misjudged something and throttled hard on the accelerator?

Sanjeev Sharma 的头像
Sanjeev Sharma2 年前

It is more of a risk for us than to anyone else. It is career at stake. We wouldn't do anything for a cheap fame. Unless the tech is rigorously tested, we wouldn't do anything. Yesterday media came to do live demo off-roads. We realized there was a construction going on with pile of mud dumped from dumper trucks on the side of the road. I gave them an explicit warning, that I know our vehicle will avoid it but this is untested. They said their safety inside our vehicle is their risk, and only then we did the demo off-roads, and did two tests, where our vehicle did avoid the pile of mud on the side, before doing a live demo with them. The vehicle was never shown before pile of muds, and our explicit obstacles detection software was also never trained for that. So whatever we do, safety is of paramount importance. Everything else can wait in life. This motion planning and decision making framework has gone rigorous testing. Our electromech system is designed as such that a safety driver can easily take control. In Bolero it is controlled by a motor which has to actuate and thus there is no electrical current based system that cannot be controlled or monitored. This is the reason why we are not running Thar in dense roads, because it has directly input to the ECU.

Akshay Charegaonkar 🇮🇳 的头像
Akshay Charegaonkar 🇮🇳2 年前

This is miraculous...your autonomous vehicle is already better than 90% of Indian drivers 🤣

Anirban Roy Das 的头像
Anirban Roy Das2 年前

When you think of driverless vehicles, one thing that never comes to our immediate attention are the roads. We are so used to seeing autonomous vehicles run in controlled environments or great quality roads. Our imagination of self driving cars are always picturing a developed nation with HQ roads, no potholes, no uneven surfaces, at least not at every 10 meters. Seeing autonomous vehicle doing path planning on Indian roads, and in this video specifically, it is as basic as it can get in terms of majority of Indian roads, is highly appreciable. Even though there are so many more variables are at play and many more things to do, this gave me such a positive kick.

Himanshu Gaurav Singh 的头像
Himanshu Gaurav Singh2 年前

Great demo! At 3:11 in the video, there is a person visible on the driving seat. Does that person manually operate the car at any point of time? If yes, then could you please elaborate which of the manoeuvres were human-assisted? Thanks in advance! Really great work!

k 的头像
k2 年前

It is doing lot of last minute obstacle avoidance. More like swaying away. Could this be a feature or you working on to make it anticipate incoming cars much faster when they are at a safer distance?

2cents 的头像
2cents2 年前

Genius

Shivam 的头像
Shivam2 年前

I believe the car's vision is able to find the traversability region and utilise it... but its still inefficient when we got a traffic scenerios and a sudden appearance of say a bike(occlusion).. that's why we are still long away from having a driverless car

VSidd 的头像
VSidd2 年前

Cool stuff. Really appreciate the people who did this work 🙏🏻

We r🤘🤘 的头像
We r🤘🤘2 年前

Awesome never seen this before and simply love 😍the Flag on the vehicle🚩🚩🚩🚩

相关视频

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
3:19

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 次观看 • 2 年前

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
5:59

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 次观看 • 2 年前

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
6:35

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

68,144 次观看 • 3 个月前

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
4:55

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 次观看 • 9 个月前

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
6:50

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 次观看 • 2 年前

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
3:38

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 次观看 • 1 年前

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
3:27

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 次观看 • 1 年前

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
1:18

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

Sanjeev Sharma

12,811 次观看 • 1 年前

Six of our team members were forced to spend the night in jail for simply denouncing the mockery of Christians by having the message "STOP ATTACKS ON CHRISTIANS" written on a bus. This is not illegal in any way, as a lawyer states: “It appears impossible to constitute the crime of failing to communicate a protest because there is no protest in the presence of one unique vehicle. The prosecutor pushed the law to its limits to stop the bus and limit their free speech. Moreover, the procedure was irregular.” In short, this is anti-Christian political and ideological persecution. #Paris2024 #Christianity
0:07

Six of our team members were forced to spend the night in jail for simply denouncing the mockery of Christians by having the message "STOP ATTACKS ON CHRISTIANS" written on a bus. This is not illegal in any way, as a lawyer states: “It appears impossible to constitute the crime of failing to communicate a protest because there is no protest in the presence of one unique vehicle. The prosecutor pushed the law to its limits to stop the bus and limit their free speech. Moreover, the procedure was irregular.” In short, this is anti-Christian political and ideological persecution. #Paris2024 #Christianity

CitizenGO

448,350 次观看 • 1 年前

Aside from his rally in Mpigi yesterday which caused the closure of roads and disrupted traffic, he also sat right in the middle of the road to speak to people while a very busy road was closed on both ends. Our motherland!
0:25

Aside from his rally in Mpigi yesterday which caused the closure of roads and disrupted traffic, he also sat right in the middle of the road to speak to people while a very busy road was closed on both ends. Our motherland!

Joel Ssenyonyi

144,859 次观看 • 5 个月前

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.
0:30

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 次观看 • 1 年前

Baza reports that a Russian blogger could face charges after putting his car on autopilot in the middle of a Moscow traffic jam and going shopping to show off on social media. Lawyers said Khizri Zapirov's stunt may have violated two traffic rules, since the driver must remain in control of the vehicle and may only leave it if there is no risk of it moving on its own.
0:57

Baza reports that a Russian blogger could face charges after putting his car on autopilot in the middle of a Moscow traffic jam and going shopping to show off on social media. Lawyers said Khizri Zapirov's stunt may have violated two traffic rules, since the driver must remain in control of the vehicle and may only leave it if there is no risk of it moving on its own.

Brian McDonald

13,478 次观看 • 10 天前

This vehicle decided it was tired of traffic and moved into the oncoming lane to pass. The strategy failed and ended up causing a traffic jam on the other side too. Watch as the traffic Karen has to back up to let them through. Why do people constantly do this? I hope this person learned a valuable lesson.
2:08

This vehicle decided it was tired of traffic and moved into the oncoming lane to pass. The strategy failed and ended up causing a traffic jam on the other side too. Watch as the traffic Karen has to back up to let them through. Why do people constantly do this? I hope this person learned a valuable lesson.

Suzee Q

61,183 次观看 • 3 个月前

"The rise in reckless driving on our roads, which is robbing our country of many lives, cannot be allowed to continue. The Zimbabwe Republic Police, along with other stakeholders such as the Ministry of Transport and Infrastructure Development, have been directed to enforce Road Traffic Regulations with greater stringency." H.E President Mnangagwa
1:57

"The rise in reckless driving on our roads, which is robbing our country of many lives, cannot be allowed to continue. The Zimbabwe Republic Police, along with other stakeholders such as the Ministry of Transport and Infrastructure Development, have been directed to enforce Road Traffic Regulations with greater stringency." H.E President Mnangagwa

Nick Mangwana

14,952 次观看 • 10 个月前

One of the complaints some FSD testers in China made is that the system is too cautious and courteous. In order to navigate through chaotic traffic, most Chinese drivers are assertive when they’re in multilane roundabouts, narrow streets and intersections where everybody forces his or her way out. Yielding to others may get you stuck in traffic. That kind of behavior is socially acceptable and nobody criticizes it. In this clip, you will find the Tesla with FSD on waited too long for the gap to turn right into a neighborhood because there was endless traffic flow of mopeds from the bike lane on the right. During the whole wait time, however, 3 other vehicles successfully made right turn by negotiating with moped operators. Will video clips like this feed into “bad behavior” of FSD after training? Each country is different and FSD needs to adapt as the old saying goes when in Rome do as the Romans do. Watch the whole video of FSD responding to the chaotic evening rush hour traffic in Guangzhou:
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One of the complaints some FSD testers in China made is that the system is too cautious and courteous. In order to navigate through chaotic traffic, most Chinese drivers are assertive when they’re in multilane roundabouts, narrow streets and intersections where everybody forces his or her way out. Yielding to others may get you stuck in traffic. That kind of behavior is socially acceptable and nobody criticizes it. In this clip, you will find the Tesla with FSD on waited too long for the gap to turn right into a neighborhood because there was endless traffic flow of mopeds from the bike lane on the right. During the whole wait time, however, 3 other vehicles successfully made right turn by negotiating with moped operators. Will video clips like this feed into “bad behavior” of FSD after training? Each country is different and FSD needs to adapt as the old saying goes when in Rome do as the Romans do. Watch the whole video of FSD responding to the chaotic evening rush hour traffic in Guangzhou:

Ray

71,941 次观看 • 1 年前

Today marked another milestone in our administration as I performed the groundbreaking ceremony for the construction of a flyover at Otovwodo Junction in Ughelli North LGA. Otovwodo Junction is a vital link to Warri, Asaba and Patani on the East–West Road Highway, making it one of the busiest roads in this axis. When completed, this project being handled by Julius Berger at a cost of ₦39.34 billion, is expected to ease vehicular movement and boost trade and commerce, with a completion period of 12 months. At the ceremony, I noted that this government is redefining the social contract with our people through compassion, conviction and a culture of accountability in infrastructure delivery. I also appealed to road users to be patient and to obey traffic controllers during this period to avoid unnecessary gridlock. I thanked the people of Ughelli Kingdom for their warm reception.
1:53

Today marked another milestone in our administration as I performed the groundbreaking ceremony for the construction of a flyover at Otovwodo Junction in Ughelli North LGA. Otovwodo Junction is a vital link to Warri, Asaba and Patani on the East–West Road Highway, making it one of the busiest roads in this axis. When completed, this project being handled by Julius Berger at a cost of ₦39.34 billion, is expected to ease vehicular movement and boost trade and commerce, with a completion period of 12 months. At the ceremony, I noted that this government is redefining the social contract with our people through compassion, conviction and a culture of accountability in infrastructure delivery. I also appealed to road users to be patient and to obey traffic controllers during this period to avoid unnecessary gridlock. I thanked the people of Ughelli Kingdom for their warm reception.

Rt Hon Sheriff Oborevwori

13,309 次观看 • 5 个月前

Danger on Duty: Barrier Dragged on Bike in Busy Traffic How are road barriers being moved on Bengaluru roads? In a recent video, it can be seen that traffic personnel are shifting a road barrier in a very unusual and unsafe manner. One person is riding a bike slowly, while another person is holding the barrier by hand and moving along with it, matching the speed of the bike. He is not exactly transporting the barrier on the vehicle, but rather dragging or guiding it while walking or running alongside, using the bike’s movement to keep it in motion.Even though the intention may be to reposition the barrier quickly, this method raises serious safety concerns. Handling a heavy road barrier in such a way requires precise balance and coordination, and any slight misjudgment can cause the barrier to slip, fall, or collide with nearby vehicles or pedestrians. On busy Bengaluru roads, where traffic conditions are already unpredictable, such practices significantly increase the risk of accidents.Moreover, this kind of improvised handling does not reflect proper safety standards. Road barriers are meant to be moved using appropriate equipment or vehicles designed for such tasks. Relying on a two-wheeler and manual handling not only puts the individuals involved at risk but also endangers everyone else on the road.Incidents like this highlight the need for stricter adherence to safety protocols and better operational practices. Public safety should always be the top priority, and such risky methods must be avoided in favor of safer and more responsible alternatives. #bangalore #bengaluru #barricade #bengalurupolice ಬೆಂಗಳೂರು ನಗರ ಪೊಲೀಸ್‌ BengaluruCityPolice ಬೆಂಗಳೂರು ಸಂಚಾರ ಪೊಲೀಸ್ BengaluruTrafficPolice CP Bengaluru ಪೊಲೀಸ್ ಆಯುಕ್ತ ಬೆಂಗಳೂರು Joint CP, Traffic, Bengaluru alok kumar DGP KARNATAKA ಕರ್ನಾಟಕ ರಾಜ್ಯ ಪೊಲೀಸ್ Karnataka State Police S. Lalitha ChristinMathewPhilip
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Danger on Duty: Barrier Dragged on Bike in Busy Traffic How are road barriers being moved on Bengaluru roads? In a recent video, it can be seen that traffic personnel are shifting a road barrier in a very unusual and unsafe manner. One person is riding a bike slowly, while another person is holding the barrier by hand and moving along with it, matching the speed of the bike. He is not exactly transporting the barrier on the vehicle, but rather dragging or guiding it while walking or running alongside, using the bike’s movement to keep it in motion.Even though the intention may be to reposition the barrier quickly, this method raises serious safety concerns. Handling a heavy road barrier in such a way requires precise balance and coordination, and any slight misjudgment can cause the barrier to slip, fall, or collide with nearby vehicles or pedestrians. On busy Bengaluru roads, where traffic conditions are already unpredictable, such practices significantly increase the risk of accidents.Moreover, this kind of improvised handling does not reflect proper safety standards. Road barriers are meant to be moved using appropriate equipment or vehicles designed for such tasks. Relying on a two-wheeler and manual handling not only puts the individuals involved at risk but also endangers everyone else on the road.Incidents like this highlight the need for stricter adherence to safety protocols and better operational practices. Public safety should always be the top priority, and such risky methods must be avoided in favor of safer and more responsible alternatives. #bangalore #bengaluru #barricade #bengalurupolice ಬೆಂಗಳೂರು ನಗರ ಪೊಲೀಸ್‌ BengaluruCityPolice ಬೆಂಗಳೂರು ಸಂಚಾರ ಪೊಲೀಸ್ BengaluruTrafficPolice CP Bengaluru ಪೊಲೀಸ್ ಆಯುಕ್ತ ಬೆಂಗಳೂರು Joint CP, Traffic, Bengaluru alok kumar DGP KARNATAKA ಕರ್ನಾಟಕ ರಾಜ್ಯ ಪೊಲೀಸ್ Karnataka State Police S. Lalitha ChristinMathewPhilip

Karnataka Portfolio

13,324 次观看 • 1 个月前

🚨 Stolen Vehicle Pursuit Ends in Arrest 🚔 Graves B deputies were alerted by a Flock camera to a stolen vehicle. Deputies quickly responded and located the vehicle, which was parked at an apartment complex. As one deputy waited for backup, the suspect unexpectedly drove off. A traffic stop was attempted, but the driver fled, initiating a pursuit. Deputies successfully deployed stop sticks, deflating some of the vehicle's tires, but the suspect continued to flee. A Tactical Vehicle Intervention (TVI) was used and was initially successful, but as deputies attempted to pin the vehicle, the suspect rammed a patrol car and kept going. A second TVI was performed and successfully disabled the vehicle. The suspect then fled on foot but was caught after a short foot pursuit. One deputy was transported to the hospital after their patrol vehicle was struck. Thankfully, they are expected to make a full recovery. A search of the vehicle revealed narcotics and other suspicious items. The driver (a Denver resident) was arrested and charged with: •Motor Vehicle Theft (F4) •Assault in the Second Degree – Peace Officer (F4) •Vehicular Eluding (F5) •Unlawful Possession of a Controlled Substance (DF4) •Reckless Endangerment (M2) •Unlawful Possession of Schedule III, IV, V Controlled Substances (DM1) •Possession of Drug Paraphernalia (DPO) •Driving Under Restraint (Traffic Offense) •Failure to Obey Traffic Signal (Traffic Offense) •Driving on Wrong Side of Roadway (Traffic Offense) •3 Counts – Violation of Protection Order (M1) •Criminal Possession of a Financial Device (M2) Additionally, the suspect had 2 active felony warrants and 2 misdemeanor warrants. “Outstanding teamwork by our deputies brought this dangerous situation to a safe conclusion. Let me be clear: criminals are not welcome in Douglas County. If you come here to commit crimes, we will pursue you, and we will catch you. We are grateful for the bravery of our deputies,” said Sheriff Darren Weekly. Note: A charge is merely an accusation, and all defendants are presumed innocent until proven guilty in a court of law. This video has been modified from its original for media release only.
1:26

🚨 Stolen Vehicle Pursuit Ends in Arrest 🚔 Graves B deputies were alerted by a Flock camera to a stolen vehicle. Deputies quickly responded and located the vehicle, which was parked at an apartment complex. As one deputy waited for backup, the suspect unexpectedly drove off. A traffic stop was attempted, but the driver fled, initiating a pursuit. Deputies successfully deployed stop sticks, deflating some of the vehicle's tires, but the suspect continued to flee. A Tactical Vehicle Intervention (TVI) was used and was initially successful, but as deputies attempted to pin the vehicle, the suspect rammed a patrol car and kept going. A second TVI was performed and successfully disabled the vehicle. The suspect then fled on foot but was caught after a short foot pursuit. One deputy was transported to the hospital after their patrol vehicle was struck. Thankfully, they are expected to make a full recovery. A search of the vehicle revealed narcotics and other suspicious items. The driver (a Denver resident) was arrested and charged with: •Motor Vehicle Theft (F4) •Assault in the Second Degree – Peace Officer (F4) •Vehicular Eluding (F5) •Unlawful Possession of a Controlled Substance (DF4) •Reckless Endangerment (M2) •Unlawful Possession of Schedule III, IV, V Controlled Substances (DM1) •Possession of Drug Paraphernalia (DPO) •Driving Under Restraint (Traffic Offense) •Failure to Obey Traffic Signal (Traffic Offense) •Driving on Wrong Side of Roadway (Traffic Offense) •3 Counts – Violation of Protection Order (M1) •Criminal Possession of a Financial Device (M2) Additionally, the suspect had 2 active felony warrants and 2 misdemeanor warrants. “Outstanding teamwork by our deputies brought this dangerous situation to a safe conclusion. Let me be clear: criminals are not welcome in Douglas County. If you come here to commit crimes, we will pursue you, and we will catch you. We are grateful for the bravery of our deputies,” said Sheriff Darren Weekly. Note: A charge is merely an accusation, and all defendants are presumed innocent until proven guilty in a court of law. This video has been modified from its original for media release only.

DC Sheriff

11,213 次观看 • 1 年前