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Swaayatt Robots is a Bhopal-based autonomous vehicle startup. They just released a demo video showing their self-driving technology at work on a single-lane, two-way road. During this demo the driverless car encountered a herd of cows, a blind corner, and a daredevil cyclist.

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10,760 次观看 • 1 年前 •via X (Twitter)

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🚨 Driverless car in a busy Indian road? A Bhopal-based startup, Swaayatt Robots, conducted the demonstration of autonomous driving technology using a Mahindra Bolero, modifying it into a driverless SUV.

🚨 Driverless car in a busy Indian road? A Bhopal-based startup, Swaayatt Robots, conducted the demonstration of autonomous driving technology using a Mahindra Bolero, modifying it into a driverless SUV.

Indian Tech & Infra

2,061,870 次观看 • 2 年前

🚨 Bhopal-based Swaayatt Robots (Swaayatt Robots) has tested their autonomous driving system while navigating two-way traffic on a single-lane road during heavy rain. The company is building Level 5 autonomous driving systems using just cameras and sensors instead of LiDAR.

🚨 Bhopal-based Swaayatt Robots (Swaayatt Robots) has tested their autonomous driving system while navigating two-way traffic on a single-lane road during heavy rain. The company is building Level 5 autonomous driving systems using just cameras and sensors instead of LiDAR.

Runtime

28,477 次观看 • 1 个月前

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

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

332,976 次观看 • 2 年前

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

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

95,684 次观看 • 2 年前

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

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

22,375 次观看 • 1 年前

Never seen a single pothole in China, their road technology is on another level. By the way, this is Wuhan, just a Tier 1 city of China

Never seen a single pothole in China, their road technology is on another level. By the way, this is Wuhan, just a Tier 1 city of China

Baba Banaras™

1,495,978 次观看 • 10 个月前

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

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

Tsla Chan

90,658 次观看 • 9 个月前

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

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

68,539 次观看 • 7 个月前

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

125,317 次观看 • 2 年前

It seems that Republicans are trying to gaslight everyone into believing that Dayanne Figueroa rammed into an ICE agent’s car… So let’s break down the video, frame by frame. 1. This is Chicago… a dense, urban area with traffic in every direction. 2. The street is two-way… you can clearly see another car driving toward the camera. 3. The ICE agent, in an unmarked vehicle, is sitting in the road. 4. They then make a very wide turn… onto the left side of the road. 5. At this point, the agent is driving on the wrong side of the road. 6. The ICE agent, seemingly unaware of their surroundings (never ideal while driving in a major city), tries to enter the correct lane… without signaling, and without checking to see if someone’s already in that lane. 7. And it turns out, someone was already in that lane… Note: A federal agent, in an unmarked vehicle, does not have the right of way unless they have their 🚨 on. The ICE agent did NOT have their 🚨 on. 8. So, the ICE agent made an improper and unsafe lane change and hit Dayanne Figueroa’s car. 9. Then, they started to drive away, but decided to kidnapped her first. Hope this helps.

It seems that Republicans are trying to gaslight everyone into believing that Dayanne Figueroa rammed into an ICE agent’s car… So let’s break down the video, frame by frame. 1. This is Chicago… a dense, urban area with traffic in every direction. 2. The street is two-way… you can clearly see another car driving toward the camera. 3. The ICE agent, in an unmarked vehicle, is sitting in the road. 4. They then make a very wide turn… onto the left side of the road. 5. At this point, the agent is driving on the wrong side of the road. 6. The ICE agent, seemingly unaware of their surroundings (never ideal while driving in a major city), tries to enter the correct lane… without signaling, and without checking to see if someone’s already in that lane. 7. And it turns out, someone was already in that lane… Note: A federal agent, in an unmarked vehicle, does not have the right of way unless they have their 🚨 on. The ICE agent did NOT have their 🚨 on. 8. So, the ICE agent made an improper and unsafe lane change and hit Dayanne Figueroa’s car. 9. Then, they started to drive away, but decided to kidnapped her first. Hope this helps.

Jesus Freakin Congress

70,221 次观看 • 10 个月前

Many people are working on building a self-driving car, but Tesla is building technology that could turn any ordinary car into a self-driving car — including millions of existing Teslas. A lot of people don't understand the difference, but it's massive.

Many people are working on building a self-driving car, but Tesla is building technology that could turn any ordinary car into a self-driving car — including millions of existing Teslas. A lot of people don't understand the difference, but it's massive.

Whole Mars Catalog

809,308 次观看 • 3 年前

Wow, just encountered a driverless car stuck on Haight, right in front of the intersection, blocking traffic. It was a normal car, but the driver was just, idk, somewhere else, not inside. While he left his car in the road. Journalists, DM for interview on this important story.

Wow, just encountered a driverless car stuck on Haight, right in front of the intersection, blocking traffic. It was a normal car, but the driver was just, idk, somewhere else, not inside. While he left his car in the road. Journalists, DM for interview on this important story.

Lee Edwards

244,452 次观看 • 3 年前

On the heels of Q2 earnings, Aurora CEO Chris Urmson joins our Jon Fortt, sharing his outlook on the self-driving tech company's road ahead: "For customers...this is a technology that can both help their topline and drive down their bottom line in a hugely meaningful way."

On the heels of Q2 earnings, Aurora CEO Chris Urmson joins our Jon Fortt, sharing his outlook on the self-driving tech company's road ahead: "For customers...this is a technology that can both help their topline and drive down their bottom line in a hugely meaningful way."

CNBCOvertime

25,456 次观看 • 3 年前

Enabling high-speed autonomous driving, with precision control, accounting for skid/drift/roll-over, on regular as well as on mountainous roads, is one of the key objectives for us in the journey towards Level-5 autonomous driving, both on- and off-roads. We are training our AI agents to execute just that ... This trailer video is a glimpse of what's there to come out from Swaayatt Robots. Enabling 90+ KM/H #autonomousdriving on a mountainous road where humans have to slow down to 40 KM/H near turns for a safe navigation. Understanding the physics, vehicle dynamics, and much more are some of main challenges for the AI agents being trained for this task ... #autonomousvehicles #reinforcementlearning #deeplearning

Enabling high-speed autonomous driving, with precision control, accounting for skid/drift/roll-over, on regular as well as on mountainous roads, is one of the key objectives for us in the journey towards Level-5 autonomous driving, both on- and off-roads. We are training our AI agents to execute just that ... This trailer video is a glimpse of what's there to come out from Swaayatt Robots. Enabling 90+ KM/H #autonomousdriving on a mountainous road where humans have to slow down to 40 KM/H near turns for a safe navigation. Understanding the physics, vehicle dynamics, and much more are some of main challenges for the AI agents being trained for this task ... #autonomousvehicles #reinforcementlearning #deeplearning

Sanjeev Sharma

29,695 次观看 • 12 天前

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

186,291 次观看 • 2 年前

Nikola Motors — which just declared bankruptcy — getting to a ~$30B valuation on $0 revenue, based on a promo video showing a “self-driving” truck that is actually just rolled downhill and powered by gravity might be the #1 ZIRP moment

Nikola Motors — which just declared bankruptcy — getting to a ~$30B valuation on $0 revenue, based on a promo video showing a “self-driving” truck that is actually just rolled downhill and powered by gravity might be the #1 ZIRP moment

Trung Phan

162,080 次观看 • 1 年前

This woman is using a bike lane as a walking lane as well. There is a car behind her trying to beat the traffic and driving in that lane. They are driving slowly behind her yelling at her to get out of the way and antagonizing her. She starts to record them because they are being reckless and have come close to hitting her and other cars. The car even cuts off the other cars as the lane is being blocked by a pole. I understand she’s mad because the car was where they weren’t supposed to be, however the bike lane is a bi-directional bike lane, it isn’t a bike lane on one side and a walking lane on the other. So who is wrong here, are they both wrong for using the bike lane when neither one is on a bike?

This woman is using a bike lane as a walking lane as well. There is a car behind her trying to beat the traffic and driving in that lane. They are driving slowly behind her yelling at her to get out of the way and antagonizing her. She starts to record them because they are being reckless and have come close to hitting her and other cars. The car even cuts off the other cars as the lane is being blocked by a pole. I understand she’s mad because the car was where they weren’t supposed to be, however the bike lane is a bi-directional bike lane, it isn’t a bike lane on one side and a walking lane on the other. So who is wrong here, are they both wrong for using the bike lane when neither one is on a bike?

SonnyBoy🇺🇸

439,793 次观看 • 4 个月前