Aisin

The corporations on our annual list posted aggregate revenues of $41 trillion for the second year in a row.

Fortune Global 500

The corporations on our annual list of the world’s largest companies delivered a record $41 trillion in aggregate revenues.

Aggregate sales hit $37.8 trillion last year—an increase of 19%, the highest annual growth rate in the list’s history.

Total revenue for the world’s biggest companies fell 4.8% to $31.7 trillion this year—the first decline in half a decade.

This year's Fortune Global 500 generated $33.3 trillion in revenues and $2.1 trillion in profits.

Here are the World's Most Admired Companies for 2019 as ranked in their industries. See who made the list.

World's Most Admired Companies

This year's Fortune Global 500 generated $27.7 trillion in revenues and $1.5 trillion in profits.

This year's Fortune Global 500 generated $27.6 trillion in revenues and $1.5 trillion in profits.

The world's 500 largest companies generated $31.2 trillion in revenues and $1.7 trillion in profits in 2014. Together, this year's Fortune Global 500 employ 65 million people worldwide and are represented by 36 countries.

Global business is back. After limping through a worldwide financial crisis and economic slowdown, the 500 largest companies ranked by revenues shattered all sorts of performance records in 2013: They racked up combined revenues of $31.1 trillion, up 2.5% from 2012, and profits soared 27% to nearly $2 trillion. China’s 95 companies (up from 89 last year) posted $5.8 trillion in revenues. The U.S. has four fewer companies on the list than last year but remains (for now) the country leader, with 128 corporations on the list—including No. 1 Wal-Mart Stores—reporting $8.6 trillion in revenues. 

Company Info

Industry

Location

Industry Rank

Previous Industry Rank

Website

Overall Score

Key Attributes Of Reputation

Innovation

People Management

Use of Corporate Assets

Social Responsibility

Quality of Management

Financial Soundness

Long-Term Investment Value

Quality of Products/Services

Global Competitiveness

Key Financials (last Fiscal Year)

Revenues ($M)

Profits ($M)

Assets ($M)

Total Stockholder Equity ($M)

Profit Ratios

Profit as % of Revenues

Profits as % of Assets

Profits as % of Stockholder Equity

Get insights into Aisin including business profile, stock information, news, and rankings from Fortune.

Aisin (ASEKY): Company Profile, Stock Price, News, Rankings

legacy

aisin

Companies

Aisin | Fortune

Aisin (ASEKY): Company Profile, Stock Price, News, Rankings | Fortune

DBS, Southeast Asia’s largest and most profitable bank, is betting big on artificial intelligence to define the next era of global banking. From algorithmic credit scoring and AI-enabled fraud detection to hyper-personalized customer engagement, the bank is embedding intelligence across its platforms to drive both efficiency and growth. With regional competition heating up and digital expectations rising, the challenge is no longer whether to adopt AI—but how to do so responsibly, securely, and at scale. CEO Tan Su Shan shares how the bank is navigating that challenge, what being AI-fueled means in practice, and how institutions across Asia can turn automation and analytics into real strategic advantage.

The Intelligence Dividend: How DBS Is Deploying AI To Reshape Regional Finance

Conferences

Artificial General Intelligence—the idea of machines that can think, reason, and learn across domains like humans—has long been a philosophical puzzle. Now, it’s becoming a technical one. From building scalable, real-world-ready systems to aligning AI with human values, builders at the frontier of AGI are facing complex design choices with far-reaching consequences. In this candid town hall, we bring together deep-tech founders, academic leaders, and provocative thinkers to explore what happens next. How close are we to scalable AGI? What are the national strategies, ethical fault lines, and cross-industry applications to watch? And what kind of future are we designing—whether we’re ready for it or not? Get your popcorn ready.

Town Hall – Beyond Human: AGI And The Future We’re Building

Artificial intelligence is transforming the foundations of global finance, accelerating innovation and challenging the way financial systems are built, regulated, and accessed. In this new landscape, the future of fintech must be global, inclusive, and collaborative by design. From real-time credit scoring for the unbanked to AI-driven regulatory sandboxes, the true promise of fintech lies not just in what it can build—but in who it can reach. How can we ensure fintech becomes a force for equity rather than division? And what steps can we take to invest in interoperable systems, embrace distributed intelligence, and embed ethics at the core of AI-driven finance?  

The Infrastructure Of Inclusion: AI, Fintech, And The Power Of Global Collaboration

We're going to turn to a conversation about how AI and global collaboration are reshaping the finance industry to be more inclusive and resilient. From expanding access to credit to rethinking regulation, we want to know more about building fintech systems that serve people, not just profits. Please welcome Global Finance and Technology Network co-founder and group CEO Satanhu Mohanty and Fortune's Clay Chandler. Thanks Andrew, um. So it's a real privilege to have Fernando here with us this afternoon. This is a man with a rare career and and a remarkable set of insights into how the world is organizing, you know, when we talk about AI and technology, we often are talking about uh developed economies. We're talking about Silicon Valley, we're talking about what's going on uh in uh in China, but there's a whole big world out there. There and a lot of people don't have access to this technology and I want to talk a little bit to Subundo uh about that but first I wonder if we could uh just have you explain a little bit about uh the global fintech uh network. This is for those of you who don't know who spent many years in the monetary Authority of Singapore and then it was uh in charge of uh Fintech for the central bank at Singapore and then. Uh, very entrepreneurial, helped the central bank develop this entity called Elivandi. Which launched the Singapore Fintech Festival. This is, I would say, probably the most successful Fintech festival in the world. It's been going on, how many years now? 10th annual. So 60,000 people descend on Singapore from all over the world for this festival and discuss every aspect. We went last year and we're thrilled to be a part of that, but it's it's. It's blockchain. It's talking about, you know, different forms of finance, but it's also made a real effort to look into the developing world, you know, the global South, and how FinTech can help people that are unbanked, gain greater access to financial markets. So I hope we can talk a little bit about that, but talk to us about uh. You know, the, the global fintech network and what it's evolved to be because it has 4 different functions, right? Oh yeah. Well, um, it's kind of an extension of my last 10 years in MAS because we are, uh, If you take the last 10 years in Singapore, we built a fintech hub, uh, which kind of expanded beyond the Singapore mandate and we went to the global ecosystem. We influenced a lot of the choices financial sector are making. So we decided that to really super scale that idea, uh, MS created this GFTN. Um, and, and the, and the core idea behind this is to create this global network of things what we are doing here, primary information, right, talking to each other at a scale so that we can capture around the world conversation around. The innovation and financial services. Not in isolation, but in partnership with the regulators, investors, founders, fintech CEOs and all. And to do at a scale is the first mandate we thought, uh, do as part of GFTN. Which is an Eliandi extension. Then we added, OK, now we have done this. Let's add the advisory piece. How do we advise low income, middle income, emerging market on a strategic policy, uh, for innovation and financial services, uh, almost replicating the Singapore success in many emerging markets. And then we bring the platforms, the public good platforms to the market, create a more connected equitable system around the world. And then we thought, OK, now we've done the first three pieces. Let's put some venture money to boost innovation to upcoming startups. This is a comprehensive ecosystem building framework. So this is really a unique entity that's part think tank, part advisor, part investor, part investor, um. And has this tremendous global reach. Now the uh Syntech festival here extremely successful, but you've extended it. You now have a version in Japan and you've also brought a version to Kigali, is that right? Tell us about that and why you've done that. Well, we wanted to take the narrative to the emerging market because it Good to have a program in Singapore or in Hong Kong or in Tokyo, but how do we take this program at this scale to emerging markets, to Africa, to Latin America, right, to deep in ASEAN, our desire is to take this conversation to every hotspot in the world because we know the future of financial services are not going to be. Created out of developed market, it will come from emerging markets. It is going to come from low income countries. They're going to redefine the future of finance with better innovation, better technology. We got to go there and have this conversation. That drove that whole network build up. So tell us where do you see some of the greatest, you know, opportunities for bringing AI and FinTech to uh empower uh the developing world. I mean, is it in India? Is it in Africa? Well, where are the kind of urgent opportunities? Well, I'm going to be a little more uh provocative here, OK, today, if you take every history of technology, we have seen different tech stack coming and solving certain piece of financial services. Now, financial sector. Historically, is the biggest paymaster for technology. Even today, look at the AI revenue breakdown. Almost 40% or 50% goes to the financial sector, but we are, but when we see our, our design, we are archaic. We are not the best. Tech tech tech stack in the world, right? So we pay the bill, but we get the bad technology. So where is the issue? The core to the issue of, of AI or any technology solving financial sector challenges is there's an elephant in the room. It is the trade market now. If you, if you say to somebody, I'm a bank, one expectation is they lend you money. Yeah. Now, when you are borrowing money from a bank, the banks ask for you from your collateral, right? Right. That's the standard process of lending. That model cannot be solving the global need for credit, right? The financial inclusion challenge is the lack of banks' ability to extend credit to low income segment, right? The low income segment has no collateral, right? What they have behavioral data, right, intend to pay back, right? They can if you give them money, they will do something about it and they'll pay you back. To me, that's the. Problem AI should solve for create credible, predictive golden source behavior data which will replace the need for a collateral for a bank to lend. If you don't solve the problem, AI is still solving the classic bank problem which is make our productivity go up. We can fire hundreds of people. I think that's not what we want AI to do. We want AI to solve the foundational credit challenge banks have. That's very interesting. So when you, when you talk about, uh, you know, solving. I mean there are two ways to see the behavioral data and I've talked to companies that are kind of viewing it both ways, but there's a set of companies who are saying, oh look, this is just about risk analysis. If we know that we're gonna get paid back our risk is 0% the lending to low income. Uh, uh, you know, uh, people, then it means that we don't have to worry about it. We can set a fair rate and, and we can lend it, but there's another category of people that I've talked to that say actually we want that behavioral data because it's valuable to us. We can use it to make a bunch of predictions. Uh, we can use it to kind of gain people's behavior. We can use it to sell it to other people. So I mean, do, are you advocating both of those things or just but I think, I think that's less of a problem today. If you ask a credit officer, they want to establish two things ability to pay, uh, intend to pay in, OK. Collaterals gives the ability to pay matrices, behavioral data gives the intent to pay matrices because the way we lend today, you go back to the default side, the non-profitting loan, all our intent, yeah, largely intent, right? So we are denying people because we don't understand the intent and at the same time we, we, we, we give people money and so that they have a bad intent later on. Now the gaming behavior comes. Also for the same segment of people who have collateral, you want them to use more of the collateral to borrow more, but banks will never lend to people who have no collateral. So my sense is that there's a two sets of problems. The bad use of data, I think we can't control because regulators have enough protections in place, but what regulators cannot. Is the bank's inability to lend to people who don't have collateral that has to come from technology that has to come from a different set of business models which I hope this AI revolution can get us there. It's really interesting. So to anchor this evaluation of behavior though, you've also got to be able to establish some firm. Identity matrix for the uh uh you know, the borrowers and, and, and consumers in, in low income countries. Now I know this because I spent a spent a fair amount of time in India and I watched Nandan Neilani build the, the Adhar biometric identity system and It's fascinating because I had several conversations with Naan about this, and he explained to me that look, you're all hung up in the developed markets about privacy. You want to protect your privacy, but you don't understand that if you're somebody out of a village, uh, that's come into the big city looking for a job in India. You don't care about privacy as much as you care about getting your identity on the grid so that you can be paid, so that you can receive government benefits, so you don't have to deal with all these middlemen and and money change money changers, uh, to get what's, what do you. Africa must be the same, right? How do we Well, if you define the last 10 years of financial sector, I say DPI, digital public infrastructure, API, how to abstract complex system and connect to third party value providers, and then comes the front end user experience, apps, the super apps, that's how defined the last 10 years, the code to. FinTech revolution was the digital public infrastructure which people like London design which took the burden of identity from corporates individuals to the state. Now the question is, do many countries trust that state to hold the identity? So the first single biggest friction to replicate the India success is the. for a state to hold the data of identity and determining your your transaction at a national infrastructure, state infrastructure sovereign level. But what is going to happen for the next 10 years is an interesting shift with the coming of the tokenization and the blockchains and the digital asset piece, we are, we are decentralizing ideas now. I think if there is a there's a mechanism of of able to take the text stack of tokenization and the ethereums of the world and create a decentralized ID on those networks, you don't need a state to control and deliver that trusted ID. So there is a hope 10 years from in the next decade to shift identity from a single single state controlled mechanism to a decentralized design, you know, in addition. Another big problem for for people from these uh emerging markets is is cross border remittances, moving money, right? They're not they they don't have bank accounts. It's not sort of simple, uh, to do, and especially when it involves going across border very challenges challenging lots of people kind of get in the middle of that and extract fees. You've had some recent success just for working with that problem. You wanna talk a little bit. I think this is the first time globally yesterday. Somebody from Singapore can use pay now, right? Go to a Fintech app, but we have now a thresh called Liquid Pay or one of them. Send money to India with a UPI ID from here with no bank detail to be to be no banks in the middle of that in the system in the user perspective, put the money put the UPI ID in a fraction of a second money goes to other side. Of course, the bank rails are behind, but the consumer experience able to send the money at a full price transparency. You know exactly what you're getting the other side with the price being displayed with a very attractive. Cost of transaction. The target is to make it below 3%, including FX, yeah, so you can send the money. It took us 3 years to build it, and it requires a great amount of regulatory policy synchronized, yeah, yes, and that that is now going to be the global template of how to connect cross-border payment across different banks using national infrastructure with regulatory policy synchron. Multiple jurisdictions Project Nexus, which BIS was heading in, it's actually going to happen in Asia. 5 Asian countries have come together, are taking this model of India, Singapore, India, Malaysia, and Singapore, Malaysia, Singapore, Thailand and creating a multilateral connectivity architecture called Project Nexus which will go live hopefully next year to do replicate at a multilateral level this whole experience of connecting. National IDs or identity reflected to different IDs to send money to each other without the complexity of banking systems. That's a huge issue. I mean, particularly within this region, right, especially emerging low income migrant workers sending money back home, right? They can now send small ticket size money without without a huge cost on the top of those transfers. Wow. So, uh, what's next for you in terms of priorities then? My biggest. Priority now is going to be upskilling. One of the single biggest challenge in our economy will be lack of job right. I think killing for the future is is an acute crisis now with AI coming, the number of jobs in the market is going to dramatically go down. If we don't upskill our people, I think we are heading to a massive economic disaster. It is already real in many countries, unofficially. 2 out of 32 out of 3 don't have a job or will not have a job, and especially for a young workforce, there's a very little hope for a job in the job market now. We got to get it right. So upskilling for the future will be my single. Biggest priority for the next 2 or 3 years. People often say AI is a very democratizing technology, but it's only democratizing if you've got skills and and I think AI can be a great tooling for our new future upskilling designs. That's fascinating. So, uh, the next, uh, FinTech festival is in November. Yes, and it's the 10th anniversary. We're going to celebrate last 10 years of super success. We're going to talk about the future 1010 years of possibility. I strongly encourage your audience to come and join us at Singapore 10th anniversary. It's very exciting. Thank you for joining us and sharing you. We really appreciate it.

The future of autonomous vehicles is here. From Qualcomm’s cutting-edge chips powering next-gen perception to WeRide’s real-world deployments across urban Asia, AI is transforming not just how we drive, but how mobility is imagined, regulated, and scaled. We unpack the breakthroughs and bottlenecks in hardware, data, safety, and trust that will define whether robotaxis become everyday reality or remain stuck in the slow lane.

The Reality Of Robotaxis

Autonomous vehicles have promised a future of convenience, but trust, policy, and performance are still major factors in how mobility is regulated and scaled. Here to discuss the reality of RoboTaxis, please welcome We Ride General Manager of Singapore, Dr. Kerry Xu, Qualcomm Technologies vice president and Q Qualcomm president of Taiwan, Southeast Asia, Australia, and New Zealand, ST Liu, and Fortune Andrew Neska. How fun was that session, right? Um, OK, we're here to talk about autonomous vehicles, robotaxis, if I may, um, so let's level set as I often like to do, um, Carrie, I'll start with you. Where are robotaxis specifically in the region? Where are we seeing them in city streets and where have they not reached yet? OK, um. Uh, I think, uh, a lot of people are already familiar with, uh, like what's happening in the US. So I focused on Asia and just, um, uh, actually I think China is uh really seeing a large scale the robo taxi deployment, uh, in Beijing, Guangzhou, uh, like we ride with in this year at February, we just launched our robo taxi fire charging commercial operations in Beijing. And we now have like more than 50 of the robotaxis providing service and in Guangzhou, same thing, we already launched our commercial charging robo taxi service and most of you may also know Baidu, I think they launched the robotaxi in Wuhan with like 100 of the vehicles. And the second, uh, hot field, uh, in Asia, I think it's Middle East, um, a lot of the you maybe all the red news we are teaming up with Uber to launch our robotaxi actually in Abu Dhabi, Dubai, and in fact we already started the driverless robo taxi test in UAE right now. Wow, that's quite, I mean just thinking about all those cities, they are dramatically different. Urban landscapes and I can't even imagine the technological challenges, but that's precisely what we're going to talk about today. Um, ST, the tech is advancing rapidly. And yet there is so much we can't do. So, again, tell us what's possible, tell us what's on the cusp of changing as it pertains to autonomy. Sure. Uh, so our company comes from uh Years of innovations and creativity in coming up with a computing efficient, very efficient computing. Uh, devices and solutions and from there also with very efficient power consumption, uh, intelligence on the edge and all that. And as the automotive progresses, the need for intelligent processing, very, very high power processing at a very efficient power consumption has become more and more important. Now using that legacy of knowledge and experience over the 40. Years or so, um, we have put together a solution, what we call like Snapdragon chassis, which, which is a solution combining all the different platforms that you would need to make a AD you know automotive driving car, autonomous driving car or AAS or anything that you need to make driving or not to drive such car very comfortable infotainment, telematics and all that. Our role is to provide such a solution so that our customers can put them into products relatively faster and then as the need for autonomous driving cars become more and more important, especially in Southeast Asia or Asia where the driving condition, the weather condition is very, very different, that solution is important because, you know, ouan will bring. And encompassing that solutions, these knowledge and then on top of that allow our OEM to expand onto it, you, you know, especially with AI become available and all that, yeah, so obviously there's a lot of technology that goes into in an autonomous vehicle, right there's the power train, whether it's electric or otherwise they're almost uniformly electric, um, there's. The sensors themselves, which are relatively straightforward affair. One thing I've noticed is, as you said, you know, the kind of ever evolving intelligence of it all perfect for this conference here, you know, it has been clear in Qualcomm's earnings reports, for example, that the company is very bullish on. Artificial intelligence moving to the edge, right, moving to these out of the data centers that we love to talk about at AI conferences like this and into these other places like cars and whatever where the power, as you said, the power and the efficiency and the size, it's all a different consideration set. How is that changing the autonomous market? Well, A lot of this decision making, the decision making process and the speed of this decision making needs to happen locally. You know, latency is extremely important. So intelligence at the at the edge is a very pivotal feature. Uh it's, it's, you know, you, you imagine driving down the road, you know, you have to make decisions very, very quickly instead of going out to the car and come back in again. That's a very critical and a very typical example of intelligence at the edge. That's that's where our forte is, you know, AI at the edge, and that is, uh, that is bringing a lot of benefits from multiple perspectives, latencies one. Um, it also helped to bolster, uh, what do you call it, the protection of information data and so on and so forth. And so that's how it is changing and, and, and with AI coming on board, the, the process of learning, the, the deductions, decision making is becoming more automated and it allows a lot of developers to come up with applications that you can actually use inside the car. So autonomous driving car is not just a car that by itself, but there are lots of things you can do inside the car which bolster the drivers or the passengers' confidence sitting in the car and all that. Yeah, and to do some of that compute um locally is candidly necessary. I live in Los Angeles. There is a mountain range that cuts through the center of LA, uh, in many of the, in many places in that mountain range, there is no cellular service. I could not imagine the car just deciding, ah, we're gonna just give up, you know, mid-drive, um. I want to come back to you, Carrie, um, what are you learning as your company deploys in these different markets? As I said earlier, very different urban landscapes, very different challenges, we mentioned weather earlier, there's the landscape itself, um. There's obviously, uh, uh, uh, you know, kind of, uh, the expectations of the drivers in a given market as well. What are you learning as your company expands? Yeah, so so far we have been deployed our erasing more than 30 cities across 10 countries. We do learn a lot of uh lessons. But I want to emphasize the three lessons we learned. The first is that we, I think we have to work closely with the government and the regulators. We monitor the regulations to make sure we actually conform to the local traffic laws and the regulations. And uh in this part we actually uh hosted many delegations from different countries um like in the a few weeks ago, I think Singapore's acting Minister of Transport Jeffy, he actually led a delegate visited our HQ in Guangzhou and At our at there, he actually announced in the next 5 years, Singapore is going to see many, many AVs in the uh on the streets and they are actually planning to integrate the Aways into the uh public transport system. So I think the government support. You know, working closely with the government is one of the things when you deploy in a different country, when no matter which country you go, the first thing is that you should work closely with the government on regulation. It's an age old technology question, but we've articulated all of the technological, technical challenges of of autonomous driving, but Given what you've just said, in your opinion, do you think the greater hurdle to full deployment are the humans or the tech? Um, from my point of view, I think technology is actually mature enough. Uh, we have seen the like operations of way more in San Francisco, Los Angeles, and like uh we ride, we are operating driverless, no safety operator on board in Beijing, Guangzhou, you know, Abu Dhabi, Dubai, so technology wise. I think it's clearly mature enough to scale, but we have to work with the government on the regulation part, public acceptance, data transparency, all this. I think it takes time to for the community to completely accept the AV as like a part of their normal life. I think it will be. There, but it's just step by step taking some time to achieve it. Everybody remembers their first autonomous ride once you do it, you know, um, SD, I'm gonna, uh, come back to you. You work with many different companies and many different geographies. Uh, same question. Do you feel like the technology is what's, uh, preventing full deployment of these things, or is it in fact the humans, the policy, the. You know, I'm gonna answer this way, we always advocate that um uh that we, we always should have a cross-indust industry benchmark transparency in the training data and uh make sure that we are compliant. To safety, to, uh, to policies, etc. etc. I think those are the things that are built into our architecture. And by doing so, uh, there's you, you build trust in people, you have to build trust in passengers, in policymakers, in general public, etc. etc. So that's part of the importance of building that into the ecosystem and it's not just a technology thing, it is a private sector having to work with the government and uh you know, work with the cities and education and so and so forth, and it comes from both the Tech technological solution for us, it comes in the form of the chip set, the SOCs and the and the software stack that our OEM partners can, you know, choose and, and, and, and, and build into their cars. But at the same time, the build the confidence, I earlier mentioned the things that you can, you can do in the car, significant increase. So take care of the safety, make sure that it is transparent and then you can enjoy yourself while you are in the car, can build up that kind of Acceptability to this whole thing, but I, you know, safety is extremely important, so we have to benchmark, we have to be transparent and so on and so forth. Yeah, yeah, and I'm hearing the word trust quite a bit at this event, so I, I understand. I have one more question for you, but I just want to tell the audience, hey, we're gonna come to you for questions next, so, uh, get ready, um, all right, so. I see you, your organization works with virtually every automaker everywhere and you're of course, you know, ultimately an American company, a global company, um, and you're seeing uh massive differences across those markets in some regard. um I'm curious how you're, you know, you're supplying these, uh, automakers with this technology and working with them to integrate it in the way that it happens. Technology has been incredibly disruptive to the automotive industry. Just speaking from the US perspective, um, Detroit has built a car a certain way for many, many, many years, and it was very, very good at that. And then Silicon Valley shattered it, just totally reversed it, you know what I'm talking about, just completely just rethought the way you would put together, uh, honestly an end point is the way you might think of it in technology, right? ST as this stuff sweeps through, are you finding with your automaker partners a difference in the way they build their vehicles, the way they incorporate this technology, and by the way, is that different in, say, China or Southeast Asia versus the US? I think the big shift to the automotive is one term that you have heard a lot about is like the software defined car. I think universally I can, I can say that all the automakers. Uh, coming to the realization that this is one area that uh everybody needs to get master in. In the past, you look at power trend, you look at the car design, you look at, you know, the things that uh automotive auto automakers will look at. Now your conversation and your considerations uh Kind of get changed a little bit and say, what is your experience when you get into the car and that's where a lot of the differentiations will be brought up as well. And so if I go into the car and then I can conduct my meetings, I can use AI to ask where I am, book my restaurants and all that, just like an extension of my office or my home. I think they will make wonders and do wonders for a particular OEM or any OEM that will, you know, that's one of the ways now to differentiate the vehicles and then on top of that, no one market, no one country, no one geography is the same. So we must enable the continuous learning and with AI being available then now you do the continuous. Learning, use the smart way to factor all these things into the solutions. I think that is the way I would answer these questions. I think the opportunity is there for everyone and I can see very clearly the realization that a software defined car is is is is that is very much with us today, so I just want to correct one thing that you said. You're not going to be going into a self-driving car and booking your meetings. We have AI agents for that. Yeah, yeah, yeah, yeah, we have AI AI that's right. We solve that right here at this event. OK, let's go to the audience for questions about autonomous vehicles and robo taxis taxis. Yes, we have one right here. We just wait for the mic to reach you, and when it does, your name and who you're with. Thank you. Thanks. Hi, uh, my name is Firoz. I'm with Syngenta. Uh, my question is, how important is the infrastructure outside the car? Quality of roads, lane markings, curb, behavior of other drivers on the road, uh, for, for it to be safe. Gentlemen, which one do you want to take that, OK? Uh, yeah, I think that's a very good question. So I think earlier Andrew mentioned is the vehicle itself must be intelligent because, uh, we put the like brains in the vehicle, but Infrastructure is also important. For example, uh, network connection is like 5G, uh, a lot of this, uh, autonomous vehicles right now still have like remote monitoring in case the vehicle stuck or in certain. Conditions, the people who are monitoring remotely can still give a hint or help the vehicle. Also, in Singapore, we are already working with the government on like how to design the pickup job point to be friendly to Aways. And how you uh monitor all the different uh pickup points to uh be able to uh see where where the vehicles, all these kinds of uh infrastructure, even the how to educate the people to better interact with the AVs. I think this is something a lot of government. Actually already start looking at and I have worked with some scholars whose study topic is how to make our infrastructure friendly to uh driverless vehicles. I think this is something is going to be happening in the next uh maybe a few years. Yeah. Very good. All right. Another question. Yes, we have one over here, right here. Just wait a moment again, your name and who you're with. My name is Selvin. I'm with Alliance Asia. Just have a question. Do you think that LIDA technologies is necessary to make autonomous vehicles more safe? Is light are necessary to make it. Yeah, um, I think, uh, from my point of view, I think, uh, LEA is very necessary. Uh, in fact, is, uh, my, my, my idea is the reason some companies don't install the ladder is probably some a few years ago, ladder is still pretty expensive, uh, sensors to install which increases the cost for the vehicles. However, if you look at the edge case, so the vehicles are different from like uh some other like your cellphone. Uh, tolerance is different for vehicles. We are not looking at 99% safety. We're probably looking at 99.9999 safety. We don't, we can't afford the accident due to age case. You cannot catch it. So cameras has uh vision based has some limitations like in the very low light environment like midnight, no light, but lighter. You know, it's uh it's emitted the light, so it doesn't rely on any lights and also the lighter can accurately detect the shape of the obstacles which also helps the algorithm to more advance the control and planning algorithm to bypass or to avoid the obstacles. So with the lighter price is cheaper and cheaper, I think. Uh, like us, we are doing is we do the fusion between the camera and the ladder. So all this allows us to detect all the obstacles in like 200 m, 300 degrees, very accurately to do the planning and controlling. So yeah, that's how I believe the ladar is still very necessary. If I just add to the conversation about the infrastructure around the vehicles. I think one of the key things going into the future is. So the cellular V to X, cellular equal to any everything. And then if you think about the future of autonomous driving, uh, CV to X is something that we in Qualcomm, uh, continue to, uh, to invest a lot in. And to your point of light, when you have CV to X, obviously you can plan ahead on many, many things and and so and so forth. So I think, uh, to the questions about the road marks and things, CV. To ask the road maps and all these, just point to the fact that we need to work very closely with infrastructure partners as well as ecosystem partners and all that. Yeah, in terms of the infrastructure, I have a very good example. I keep telling my friends is that right now in the car park in certain areas we install mirror for the help drivers to look at the blind spot, but in the future for avis actually we actually can install. Those B2X equipment to help the AVs to look at the black spots. I think these are all the future probably development for the infrastructure to be AV friendly. But it is unquestionable that our our urban landscapes are going to change dramatically because of of the rise of robotaxis and autonomous vehicles. So gentlemen, thank you so much for an enlightening conversation, a round of applause please for our panelists. Thank you. Thank you.

Aisin

Aisin in Coins2Day Rankings

Aisin stock chart

Videos