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Autonomous driving through tight, dynamic, stochastic, and adversarial traffic-dynamics on sub-urban roads in India, as well as through partially unstructured environments. This demos showcases the robustness of our motion planning and decision making algorithmic frameworks in enabling #autonomousdriving through seamlessly through such traffic and environmental scenarios. The vehicle starts...

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Sanjeev Sharma

6,517 subscribers

186,204 Aufrufe • vor 2 Jahren •via X (Twitter)

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10 Kommentare

Profilbild von Arturo Deza
Arturo Dezavor 2 Jahren

Congrats! Very cool demo @sanjeevs_iitr ! We are currently working on a similar approach in Lima, Peru!

Profilbild von sphere
spherevor 2 Jahren

You deserve national media coverage.

Profilbild von Sanjeev Sharma
Sanjeev Sharmavor 2 Jahren

@tanmay976 Thank you so much Tanmay! I hope when I have done enough credible work I may get that :) If people do not know us, it means we have not yet done enough.

Profilbild von arun kumar singh
arun kumar singhvor 2 Jahren

I think this is a better demo than what people showcase with Tesla.

Profilbild von Adithya Thatipalli
Adithya Thatipallivor 2 Jahren

Autonomous driving car testing, training with Indian roads will be the most accurate data to increase preciseness

Profilbild von Pratik Desai
Pratik Desaivor 2 Jahren

This is good.

Profilbild von Ajinkya 🕹️
Ajinkya 🕹️vor 2 Jahren

India is not for beginners; you took this to entirely next level.

Profilbild von gaurav
gauravvor 2 Jahren

Incredible milestone, kudos!

Profilbild von Niamato Inc
Niamato Incvor 2 Jahren

Congratulations, Sanjeev Sharma, on a groundbreaking demonstration! Your work is steering the future of #autonomousdriving into reality, navigating the complexities of real-world chaos with grace and precision. Your vision for Level-4 autonomy by 2024 is not just ambitious, it's revolutionary. Keep paving the way! #Innovation #MachineLearning #SwaayattVehicles

Profilbild von Anjanikumar
Anjanikumarvor 2 Jahren

Next level tech,fantastic.

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Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning
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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 Aufrufe • vor 2 Jahren

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

125,218 Aufrufe • vor 2 Jahren

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
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Sanjeev Sharma

68,144 Aufrufe • vor 3 Monaten

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Presenting autonomous driving in complex, stochastic and adversarial traffic-dynamics, on the roads in #India. Over the course of last year, we enabled #autonomousdriving in conditions and in situations no one in the autonomous driving industry considered was possible, and in a country like India, where the contemporary belief was that #autonomousvehicles are an impossibility. With the closure of 2023, we present autonomous driving at a very large scale, in the city of Bhopal in India. In this demo our autonomous vehicle at Swaayatt Robots can be seen negotiating the surrounding traffic with ease, where it had to deal with their stochastic and adversarial driving patterns, like suddenly switching lanes and appearing all of a sudden in our vehicle's current driving lane, without adherence to the traffic rules. This demo was a culmination of the cutting-edge research we have been doing, and the technologies we have been developing, over the years, which we showcased throughout in our demos in 2023 -- campus autonomous driving (February), off-roads autonomous driving (April and September), tight-stochastic traffic negotiation (August and September), bidirectional traffic negotiation on a single lane road (October), large-scale city level demo (November), and Toll-Plaza negotiation (December). In 2024, we will scale our technology commercially, and bring it to North America and Europe, to topple this trillion dollar industry, and will also scale our technology throughout India. Wishing everyone a very Happy New Year! #deeplearning #reinforcementlearning #MachineLearning Elon Musk PMO India Narendra Modi Nitin Gadkari DARPA DRDO

Sanjeev Sharma

95,416 Aufrufe • vor 2 Jahren

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
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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 Aufrufe • vor 9 Monaten

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 Aufrufe • vor 1 Jahr

Enabling autonomous vehicles perceive their environment using only off-the-shelf cameras has been a long term research objective at Swaayatt Robots. This demo highlights the capabilities of our on-road perception system which is able to detect obstacles, road boundaries, lane markers in images, as well as compute depth of the complex scenes in the environment. The output shown in this video is end-to-end raw output from our deep learning system, without any post processing. The current system, with joint computation of obstacles, lane/road boundaries, and depth, works at 30 FPS on an embedded GPU in our autonomous vehicle, and can achieve higher FPS with further optimization -- which is currently a research in progress. This system is being scaled up for both the day and night operations, and we will showcase its strength towards enabling autonomous driving on a mountainous environment with unpaved roads, in the absence of any delimiters. #deeplearning #autonomousdriving #autonomousvehicles #machinelearning
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18,478 Aufrufe • vor 1 Jahr

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

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12,811 Aufrufe • vor 1 Jahr

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.
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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 Aufrufe • vor 3 Monaten

Crazy traffic jam at Dahisar toll plaza on both sides. Entry/Exit to the financial capital of India is in shambles. Vehicles will take more than 30 minutes just to cross this maddening toll plaza. Traffic backlog till Borivali. Main reasons: Existence of the toll plaza, poor roads, illegally parked trucks, incomplete work by MMRDA metro line 9, and traffic signal at Penkarpada traffic chowky near Nike Showroom.
1:05

Crazy traffic jam at Dahisar toll plaza on both sides. Entry/Exit to the financial capital of India is in shambles. Vehicles will take more than 30 minutes just to cross this maddening toll plaza. Traffic backlog till Borivali. Main reasons: Existence of the toll plaza, poor roads, illegally parked trucks, incomplete work by MMRDA metro line 9, and traffic signal at Penkarpada traffic chowky near Nike Showroom.

Jeet Mashru

15,555 Aufrufe • vor 1 Jahr

This is my second video clip showing a full autonomous drive by a Tesla Model Y from the Giga Texas factory exit, through traffic (these are non-pubiuc roads) about 1.5 miles (~2.5 Km) to the west End of Line facility. This is more of a side view and shows how the cars get from the end of the autonomous drive into the EOL.
3:14

This is my second video clip showing a full autonomous drive by a Tesla Model Y from the Giga Texas factory exit, through traffic (these are non-pubiuc roads) about 1.5 miles (~2.5 Km) to the west End of Line facility. This is more of a side view and shows how the cars get from the end of the autonomous drive into the EOL.

Joe Tegtmeyer 🚀 🤠🛸😎

100,163 Aufrufe • vor 1 Jahr

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0:23

JUST IN: President Museveni has arrived at Queensway to commission Lot 1 of the Kampala Flyover Project, marking a significant milestone in Uganda's infrastructure development. This project will greatly contribute to the country's economic growth by improving urban transportation, reducing traffic congestion, and promoting industrial growth. The Kampala Flyover Project aims to decongest the city by separating through-traffic from city-street-traffic, making it easier for people to move around and do business.

Sandra Mwesiga

26,495 Aufrufe • vor 1 Jahr

Putting my feet up on vacation. “I love to drive” people told me when I did consumer research about autonomous vehicles a decade ago. In Los Angeles traffic? now I understand why you are so bullish about FSD. The traffic in Los Angeles is next level. Manually driving in this sucks. Headed to Scottsdale, Arizona to do some fun manual driving to Zion National Park on roads that don’t have traffic. Where driving is fun, in small doses. :-) Oh and don’t look at your 401k. Put your feet up instead and enjoy the weekend! :-)
0:45

Putting my feet up on vacation. “I love to drive” people told me when I did consumer research about autonomous vehicles a decade ago. In Los Angeles traffic? now I understand why you are so bullish about FSD. The traffic in Los Angeles is next level. Manually driving in this sucks. Headed to Scottsdale, Arizona to do some fun manual driving to Zion National Park on roads that don’t have traffic. Where driving is fun, in small doses. :-) Oh and don’t look at your 401k. Put your feet up instead and enjoy the weekend! :-)

Robert Scoble

151,635 Aufrufe • vor 1 Jahr

Gauteng roads are riddled with potholes, with traffic and street lights barely working most of the time. The result leads to extensive vehicle damage and lost productivity due to traffic jams. Not to mention the lack of safety at night in the community. #SouthAfricaTonight #DStv403 #QuestionThinkAct
2:59

Gauteng roads are riddled with potholes, with traffic and street lights barely working most of the time. The result leads to extensive vehicle damage and lost productivity due to traffic jams. Not to mention the lack of safety at night in the community. #SouthAfricaTonight #DStv403 #QuestionThinkAct

eNCA

43,622 Aufrufe • vor 1 Jahr

The traffic in Kampala and Uganda is a mess; a few years down the road when we finally wake up, we’ll spend billions to hire the best urban traffic and road safety experts to tell us how we got here and billions more on how to get out of this mess. Today we’re watching everything slide and we seem not bothered as the mayhem claims more of our family and friends. Why? Who’s interested in the mess on the roads? How do we find peace in such a mess? 🎥 Joel Mwesigwa
0:24

The traffic in Kampala and Uganda is a mess; a few years down the road when we finally wake up, we’ll spend billions to hire the best urban traffic and road safety experts to tell us how we got here and billions more on how to get out of this mess. Today we’re watching everything slide and we seem not bothered as the mayhem claims more of our family and friends. Why? Who’s interested in the mess on the roads? How do we find peace in such a mess? 🎥 Joel Mwesigwa

Joseph Beyanga Joe Walker

15,729 Aufrufe • vor 2 Jahren

We’re laying the foundation of a Smart Traffic Management Centre at the famous City Cabanas — a system that will monitor movement across the city and deploy traffic cameras at key corridors. The goal is simple: make Nairobi move. #KURA #SmartCityKenya State Department For Roads Kenya Urban Roads Authority (KURA)
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We’re laying the foundation of a Smart Traffic Management Centre at the famous City Cabanas — a system that will monitor movement across the city and deploy traffic cameras at key corridors. The goal is simple: make Nairobi move. #KURA #SmartCityKenya State Department For Roads Kenya Urban Roads Authority (KURA)

𝗘𝗡𝗚. 𝗦𝗜𝗟𝗔𝗦 𝗞𝗜𝗡𝗢𝗧𝗜 , 𝗘𝗕𝗦

21,770 Aufrufe • vor 7 Monaten

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

Davis Chirchir, EGH

24,595 Aufrufe • vor 5 Monaten

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

Qace Dynamics

12,654 Aufrufe • vor 6 Monaten