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Analysis EO
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Analysis EO
Jul 10, 2020 11:39 am ·

Time for BAT and TMD to Hit the Driverless Car Road

► The large addressable market and potential revenue synergy is luring BAT (Baidu, Alibaba and Tencent) and TMD (Toutiao, Meituan Dianping and DiDi) to join the driverless car game. DiDi's in-house Autonomous Vehicle (AV)  in Shanghai is accelerating the adoption curve and also heating up the game, making the rest of the names and startups at times breathless.  ► Pure-tech companies in the area have technology solutions but are struggling to step into the growth/mature stage – and with the unviable business model are exposed.  ► The funding market has been cooling down for a while, with some unicorns suffering without new support. Investors are asking for more – and autonomous driving startups suffered most.  ► As massive – in terms of scope, size or capacity – Level-4 AV deployments are at least four to six years ahead, a ticket to the Internet giants' boat is not a bad exit.  2020 was an eventful year that saw increased AV adoption across some tier-1 cities  China saw several Level-4 major deals closed in the first half of this year – DiDi AV spinoff (USD 500 million), Pony.ai (USD 462 million, Series B), Inceptio.ai (USD 100 million).  The descending enthusiasm of investors could have resulted from the repeatedly postponed commercialization timeline of AV technology. Both giant companies like Google's Waymo and ambitious startups as Momenta once claimed that they would materialize mass production of Level-4 autonomous driving vehicles by 2020. Yet, no single company has realized the goal, due to immature technology, stubbornly high costs and inadequate regulations.  While the investors are getting more discreet on their bets, their expectations remain high. Though the number of deals lessened in the past two years, the volume of money raised in each deal is getting higher. At the beginning of this year, Chinese AV startup Pony.ai secured USD 500 million from Toyota, yet another industrial investor following Kunlun (300418:SZ) – a gaming company. The injection will sustain the firm's research on L4 in the coming years but might harm the company's independence, in our view.  L4 tech solutions providers need to reconsider their role – RoboTaxi operator, self-driving car maker or tech providers. Choosing the latter means they only earn licensing fees.  It might be hard for driverless technology alone to take a majority portion of ride-hailing trips while the rest relies on customer service, as Waymo executive John Krafcik implied. Leading companies have been operating their driverless fleet in China on a small scale. For instance, WeRide reported a total of 8,396 orders of its RoboTaxi service to Guangzhou citizens, in December 2019. However, point-to-point operations in some urban areas are still the initial stage of commercialization.  Like Waymo, Chinese VC Blue Run Capital expressed a similar opinion. OEMs, software integrators and channels surrounding the core OEMs are their priority for opportunities of artificial intelligence (AI). OEMs integrate upstream, downstream and third-party resources efficiently. In the direction of AV, those who focus on parts of the value chain can fonds the course hard, as one closes the loop of demand and supply, creating less value. The company has invested in Lixiang four times, the next being – maybe – China EV stocks after NIO (NIO:NYSE).  Who's the next in Internet giants' shopping bags? Internet/industrial conglomerates have an endless appetite for cutting-edge technologies due to the fear of missing out (FOMO). Their deep pockets support the money needed for acquiring the share of a business when they feel there can be a possible revenue synergy going on.  In the auto industry, whose history is almost a history of M&As, we saw many mega-deals happen in the past five years. Chipmakers and tier-1 suppliers – sensitive to the shifts of world science and technology – are engaging in the game. Intel's USD 15.3 billion acquisition of Mobileye and Delphi's several deals is a clear sign. Pure-tech companies that have technologies but are struggling to step into the growth/mature stage and find the unviable business model are being exposed. A leaf in the storm  We view DiDi's driverless service launch in Shanghai as a significant milestone for the auto industry and, at the same time, a considerable challenge to startups in the same vein. DiDi's peers – not smaller ones in the ride-hailing niche but tech giants – will react accordingly, as the cost of missing new chances may be infinite, just as Baidu missed the opportunity of mobile apps and content recommendation in the 4G era.  The large addressable market and potential revenue synergy is luring BAT (Baidu, Alibaba and Tencent) and TMD (Toutiao, Meituan Dianping and DiDi) to join the driverless car game. Meituan, for instance, has been developing and investing in last-mile delivery AVs to better support its food delivery segment. Its new bet on Lixiang shows its ambitions in networked mobility as well.  The greatest strength for Internet giants to rule the AV business is the solid user foundation created by their primary business. ByteDance (BD), for instance – the Daily Average User (DAU) of its hottest app, Douyin (China’s counterpart to TikTok), reached 400 million as of January, the number having hit 900 million during China's lockdown. The advantage that BD has on traffic entry and its intelligent recommendation systems is paving the way to the Internet of Vehicles (IoV). It will take full advantage of in-car times of drivers and passengers by providing short-video content and expects to commercialize from advertising.  Alibaba has made a presence on the upper stream, investing/building ventures of HD map (AutoNavi) and IoV/V2X (Banma Network). E-commerce giants like Alibaba and JD.com all research on autonomous long-haul freight where L4 Autonomous Truck companies like TuSimple Inceptio and Plus.ai leads the game.  The bottom line As DiDi shows a clear mission to envisage itself as operating fleets of autonomous robotaxis in the next ten years, BAT and TM need to consider engaging more in the game. The need to understand who develops owns and operates the driverless robotaxis or trucks and the surrounding systems, and further, how their advance computing capabilities will help or hinder their entry into the market with their more-than-ten-billion customers, is crucial. They can provide the whole autonomous network with the required infrastructure and best customer experience and move the needle for the autonomous driving industry. 

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Analysis EO
Nov 29, 2019 07:18 pm ·

WIM Salon | Future Mobility:Transportation and Mobility Revolution in China

Autonomous driving is a disruptive innovation that seems certain to change the automobile industry sometime in the near future. For some time now, there has been talk about how the global autonomous driving market is poised to stimulate dynamic growth – while obliging carmakers, owners and governments to adapt. Normally, the autonomous vehicle – or as many call it, the self-driving car is a vehicle that is capable of sensing its environment and moving safely on its own. On Nov 24, EqualOcean welcomed autonomous driving industry insiders and investors to WIM Salon, where they discussed the possibility of future mobility. Panel discussion The ‘mobility 4.0 revolution’ as a part of the 4th industrial revolution, is changing the rules of the transportation game and modern life. EqualOcean has combed the most-funded autonomous driving startups in China and we found that in China, both startups and the capital market have shown strong interest in this field since 2014. The discussion at our WIM Salon started with: How do you see china's revolution in the transportation and mobility industry? Co-founder and CEO of Lidar developer ZVISION Technologies Co., Ltd (北京一径科技有限公司), Mr. Shituo (石拓), said that transportation and mobility in the past 5 years have been impressive. Infrastructure has also improved, he added, which has helped accelerate this technology on the market. ZVISION is a Beijing-based company aiming at producing high-accuracy and performance lidar that everyone can deploy. Currently, it has completed three rounds of financing and is seeking to expand in Jiangsu to achieve mass production next year. As the General Partner of Vertex Ventures (祥峰投资), which has focused on AI, transportation, mobility and circulation industries since 2009, Mr. Xia Zhijin (夏志进) shared his opinion as an investor: “Autonomous driving will take longer to implement and commercialize in China or the US, but we are already seeing some of the startups in China doing well by providing ADAS or DMS solutions to OEMs. Personally, I am very optimistic." He also strengthened that regional diversity, such as the Mobike or Ofo story, cannot be easily copied in Europe, which means startups should think about the special situations that need to be solved based on local demand. Co-founder of Evoke Motorcycles, Mr. Sebastian Chrobok, also said that the revolution has been huge. He has been in China since 2005 and seen a huge expansion in last-mile transport and cross-country transport. Evoke is an OEM that makes cutting-edge motorcycles powered by battery. They found that the most interesting revolution in China is the speed of charging station growth. Enabling a car to run with little or no human input is much more complex than most people imagine, since the three key processes of an unmanned vehicle require advanced technologies. So how AI chips and algorithm has changed the autonomous driving in many ways? Mr. Shi: "I believe finally the massive deployment of AI in autonomous driving would be edge computing, which means all AI chips are deployed in the vehicles. So we also need to reduce the power comsumption of AI chips as we increase the computing power. In this way, I think the AI chip complexity should be simplified, while providing more computing power at lower power consumption for the massive applications in autonomous driving.” Mr. Xia: "We need more powerful chips in order to process the data that are generated by all the sensors. Therefore you need those chips in the car, not the cloud. Cloud computing takes 100s of milliseconds to process the data and send it back to the car, which is bad. Therefore we need an embedded system in the car to make the computing process instant. In the future, V2X infrastructure will become very important for autonomous driving. Roadside cameras, lights, etc. will be fed to your car which can help make decision making in terms of speed." Mr. Sebastian strengthened the importance of hardware; the majority has to happen in the hardware. “Vehicles in the past were still Windows 95 under the hood, and were not designed for mass computation. What they wanted to do at Evoke was take the internal computations of the car and put it into a bike. Therefore, the chipsets were not designed for the motorcycle conditions they wanted to put them in." In terms of the hardware, what are the pros and cons of lidar and camera? Mr. Shi believed that ultimately LiDar will be the future, especially for a higher level of autonomous driving: "Two campaigns are ongoing right now (Waymo vs. Tesla). They make their decisions based on the level of their autonomous solutions. Waymo needs to ensure 100% safety for passengers while Tesla takes less responsibility.  Mr. Xia thought that startups may not be able to become big players in autonomous driving as OEMs but can become OEMs’ suppliers: "we need capable hardware before we can have a sound autonomous driving system. It could take longer to see the commercialization of autonomous driving and is still very premature. It might still need 5 years to be able to see commercialization.”   Mr. Sebastian was not going to speculate for LiDAR or Camera: "They do very basic sensor technology. Autonomous driving, as a motorcycle rider, is scary because of how they are programmed and we need to be more concerned about 'what if' situations, like the acceleration rate of the motorcycle vs the autonomous software. Therefore, the human factor is the hardest to understand for these data-driven tech companies." Trucking or Robotaxi, which one will be the first form of automotive autonomy to take off commercially? Trucking and passenger Robotaxi are the most popular landing scenes for autonomous vehicles now – e.g. Waymo, the self-driving unit of Alphabet, which is focusing on ride-hailing, and TuSimple (图森未来) which focuses on developing Level-4 (SAE-standard) commercial autonomous driving truck solutions. The question of which will arrive earlier has been widely discussed. Mr. Xia thought that Robotaxi is the better choice and more feasible within cities because trucks face very different conditions on both highways and cities in China. Mr.  Shi believed other scenarios will commercialize even faster, such as delivery, road cleaning and trucks in harbors – those applications may ramp up in 2 years. Mr. Sebastian thought it will depend on the environment; the first arrival will happen in well-prepared industries with demand behind them.  Speed up the process Through 2018, we have seen a huge acceleration in investment in autonomous vehicle technology. According to KPMG, Europe and North America are more ready for this new mobility. The regional difference is huge, which shows the importance of testing, validation and homologation. The arrival of fully autonomous cars might be some years in the future, but companies are already making huge bets on what the ultimate archetype will look like. To speed up, we need more substantial progress on fundamental fronts – in the legal framework, infrastructure, investment and consumer acceptance, which will speed up the arrival of autonomous technologies. Another way to speed up the process, according to Mckinsey, would be to make the shift to integrated system development. Instead of the current overwhelming focus on components with specific uses, the industry needs to pay more attention to developing actual systems, especially given the huge safety issues surrounding autonomous vehicles.  World Innovators Meet (WIM) 2019 will kick off the automotive salon on Sunday, November 24th, 2019. The WIM Salon will then end with a big year-end party on Dec. 22, 2019.  Don't miss out and be sure to scan the QR below for more information!   

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Analysis EO
Jun 27, 2019 11:29 am ·

Meet the Most-funded Autonomous Driving Startups in China

With the accelerated penetration of artificial intelligence, autonomous driving technology has undoubtedly become the hottest field at present. In addition to traditional automobile enterprises and Internet giants, a large number of technology startups have also emerged. According to the incomplete statistics, there have been more than 240 startups globally involved in R&D activities in the field of autonomous driving technology. China is the largest automobile market in the world, no wonder that the number of local startups is high. Funding activity is on the up Being one of the most promising sections of the automobile industry, the autonomous driving market starts to heat up gradually. Data from ITjuzi shows that as early as 2008, there were startups related to autonomous driving technology in China. The pace of development entered the fast lane in 2013 and peaked in 2017. After several years of inactivity, capital markets in 2015 showed unprecedented enthusiasm for autonomous driving. ITjuzi (IT桔子), a venture-capital database in China's pan-TMT sector, released data showing that 8 autonomous driving companies in China received some funding in 2014. In 2015, this figure soared to 31, with a growth rate of 287.5%. The amount of investment also increased from CNY 320 million to CNY 4.46 billion in this year. Of course, the rapid development of the industry cannot happen without policy support and catalysis. Investors are keenly aware of opportunities in the industry behind policy support. ‘Made in China 2025 strategy’ was officially promulgated in 2015. China needs to master the overall technology and key technologies of intelligent driving by 2020. The company will master the overall autonomous driving technology and key technologies, establish a completely independent research and development system, production supporting system and the industrial cluster of the intelligent connected vehicle, and basically complete the transformation and upgrading of automobile industry by 2025. China's ‘medium and long-term development plan for the automobile industry’ has proposed specific time nodes for the development of intelligent connected vehicles in 2017. The assembly qualified rate of new car DA (driving assistance), PA (partial automatic driving) and CA (conditional automatic driving) systems should exceed 50%, and the assembly rate of connected driving assistance system should reach 10%. By 2025, the assembly rate of DA, PA and CA new cars will reach 80%, among which the assembly rate of PA and CA new cars will reach 25% by 2020. In other words, autonomous cars will begin to enter the market just in several years. Well-funded Research from Strategy Analytics shows that the market size of autonomous smart cars and mobile Shared travel will reach USD 7 trillion by 2050, including USD 4 trillion for ride-hailing services based on driverless smart cars and USD 3 trillion for express delivery and commercial logistics services. Against this backdrop, China's massive market could spawn more autonomous driving startups. As for the popularity of autonomous driving, some insiders believe that autonomous driving technology has a large market space and a long industrial chain, and will gradually become a new pillar industry in the future, which is bound to produce new super enterprises. In addition, the application of Internet of things, big data, AI and other technologies is accelerating the transformation of the logistics market to intelligent logistics, and the demand for autonomous driving, distribution technology and solutions in the logistics scene also triggers the further increase of capital and start-up companies. Changing the face of transportation is a costly business, one that typically requires corporate backing or a lot of venture funding to realize such an ambitious goal. On Jan. 4, WeRide.ai secured the Series A funding round of tens of millions of dollars from Sensetime (商汤科技) and ABC International (农银国际). That was the first publicly announced round of financing in the autonomous driving industry this year.  Another Chinese autonomous driving startup TuSimple announced on February 13 that it has secured a USD 95 million Series D financing led by Sina capital, which will be used for the commercial landing, technology research and development of autonomous driving. Toussaint future has raised about USD 85 million in the first three rounds, while TuSimple says it is valued at more than USD 1 billion after the Series D round financing. In addition, there are many other companies that have received significant financing since their inception. EqualOcean has compiled a list of the top 10 companies that have raised the most money so far. Top 10 Pony.ai Pony develops artificial intelligence solutions in the field of robotics. James Peng and Lou Tiancheng founded it in 2016, with its headquarters in Fremont in California with additional offices in Silicon Valley, Beijing, and Guangzhou.  Pony.ai is so far the best-performing Chinese autonomous vehicle company, ranking fifth with 1,022.3 MpD (Miles per Disengagement) in the annual autonomous vehicle testing report released by the California DMV. Horizon Robotics As one of the world's leading AI startups, Horizon Robotics has a top executive team in the industry, with the ability to develop algorithms, software, hardware, processors, and cloud infrastructure. Of the world's top four Internet AI R&D institutions, two were established by the founding team members of Horizon Robotics - Baidu's Institution of Deep Learning (IDL) and Facebook AI Research (FAIR). The company's core members have been rated world No.1 in more than 20 international AI competitions, and have provided products and services that have been affecting hundreds of millions of users in such areas as processor design, high-performance cloud computing, parallel computing, face recognition, and voice semantic recognition.  Tusimple TuSimple, which launched in 2015 and has operations in San Diego and Tucson, Ariz., has been running daily routes for customers in Arizona. The company recently raised $95 million in a Series D funding round led by Sina Corp. The company is preparing to scale up its commercial autonomous fleet to more than 50 trucks by June. TuSimple has raised $178 million to date in rounds that have included backers such as Nvidia and ZP Capital. Sina, the operator of China’s biggest microblogging site Weibo,  is one of TuSimple’s earliest investors. Momenta Momenta is an autonomous driving company from China and aims to build the ‘Brains’ for autonomous vehicles. The company was founded in September 2016 by Cao Xudong, a former scientist at Microsoft Research and formerly executive director of research and development at Chinese face recognition start-up SenseTime. Momenta's deep-learning based software in perception, HD semantic mapping, and data-driven path planning enables the realization of full autonomy. AutoAI Founded in April 2018, AutoAI was established by Navinfo, the original intelligent network business, and many domestic first-class Internet companies and well-known investment institutions. Invested and jointly invested in the establishment of an intelligently networked system developer and operator for a new generation of self-driving cars. The company has gathered many well-known leading figures and talents in various sectors of the industry, providing technology development and product development from intelligent navigation, car networking services and content, intelligent networked operating systems and solutions, car networking big data and operation. Robosense RoboSense (Suteng Innovation Technology Co., Ltd.) is a world-leading LiDAR environment perception solution provider. Founded in 2014, RoboSense is headquartered in Shenzhen and has set up branch offices in Beijing, Shanghai, Germany, and the United States. The company is committed to delivering comprehensive LiDAR environment perception solutions, focused on which, the team has been continuously working on and making innovations in many core technical fields such as FPGA, LiDAR hardware, and AI algorithms. With customers’ needs in mind, RoboSense provides a variety of bespoke intelligent environment perception LiDAR systems. Hesai Hesai specializes in designing and manufacturing laser sensors for different industries, including LiDARs (3D scanners for self-driving cars and robots), and gas leak remote sensors for the natural gas industry. Hesai was founded in 2013 in Silicon Valley and now is headquartered in Shanghai, with about 560 employees (>30 PhDs) from top universities such as MIT, Stanford and Tsinghua, and industry-leading engineers from top tech companies such as Apple, Samsung, BMW, Delphi, etc. Hesai currently owns 1 research centre and 2 manufacturing centres. Hesai's LiDARs have served pioneering autonomous driving technology companies worldwide. More than 40% of 62 companies who have autonomous-driving road-test permits in California are Hesai's customers. Holomatic Founded in June 2017, HoloMatic is a startup company dedicated to providing autonomous driving solutions based on cutting-edge artificial intelligence and automotive industry technologies.  HoloMatic is one of the few companies in possession of a complete layout of autonomous driving technologies, from the wire control, multi-sensor to core algorithm modules. Meanwhile, the company provides solutions targeted at mass production, as an effort to accelerate the industrialization of autonomous driving. Yihang.ai Yihang.ai, a Changchun, Jilin Province-based startup focused on providing autonomous driving solutions, announced on October 13 that it has signed a strategic cooperation agreement with smart EV startup CHJ Automotive in the autonomous driving field. The first volume production SUV from CHJ Automotive will adopt an Autopilot solution jointly developed by the two companies, who will also work together to develop Level 4 autonomous driving technology based on deep learning and aim to launch China’s first L4 capable volume production vehicle. Active investors After policy guidance and capital input, how to commercialize and land their autonomous driving technology becomes the focus of the industry. According to the current market situation, the start-up companies with advanced driving assistance system (ADAS) are similar to traditional parts companies in terms of commercial landing. One is to cooperate with agents to enter the c-terminal consumer market in the future.  At present, such enterprises have received some market orders. Some enterprises with core technology algorithms provide software technology and calculation methods to the car factory. Such enterprises usually need to run in and cooperate with the car factory for a long time。This is not difficult to explain the head of the investors is not only investment institutions, but also many car companies involved.  EqualOcean collated and analyzed the financing histories of these ten companies and found that, in the current financing history of these ten enterprises, there are 54 investment enterprises, among which the relatively active enterprises include IDG Capital, NVIDIA, NIO Capital, Sina, Tencent, and Sequoia Capital China. In TuSimple's five rounds of funding, Sina appeared three times. NIO Capital is an investor in two companies, Momenta and AutoAI. Yet immature but promising industry Some industry experts say that the trucking is expected to be one of the first major areas to commercialize autonomous driving vehicles or adopt autonomous driving technology, as highway conditions have relatively predictable advantages over busy urban streets. Many companies are also engaged in the field of commercializing autonomous driving vehicles. Maybe that's why Sina is so confident about TuSimple. Daimler, for example, recently announced its plans to invest about EUR 500 million (USD 573 million) over the next few years to develop highly automated trucks. At the same time, Stuttgart, Germany-based automotive giant made its first-ever investment in a Chinese startup this week, taking part in a USD 46 million funding round for Momenta, a Beijing-based firm providing road sensors and high definition mapping software. In the future, autonomous driving technology might bring a lot of convenience to people's mobility and significantly improve driving safety. However, it should also be noted that it is not easy to achieve fully autonomous driving, and it still faces many challenges, including technical, legal, infrastructure, maps and high-end sensors. At present, the autonomous driving research and development of startups and even car companies are still dominated by prototype technology solutions, which are still a long way from actual landing and mass production applications.

Analysis EO
Analysis · 2
Analysis EO
Mar 23, 2019 10:22 am ·

China’s Driverless Car Race Backfires

The most valuable autonomous driving company, Waymo, is reported to seek outside investment these days. It began to sell its own custom Lidar by The Information, roiling the global autonomous driving market. In China, NIO (蔚来) backed-Momenta (初速度) was said to cut 60% of programs and move R&D to Suzhou in 2019. In addition, Roadstar.ai fired its co-founder and chief scientist, ZHOU Guang (周光), due to dishonesty and cheating. Investors poured loads of money into this driverless car race in hopes that their startups may cross the finish line first. However, is the market beginning to backfire? We took a look at a load of autonomous car startups that have raised over USD 100 million. The cohort only focuses on startups working on its own tech to break into the passenger L4 autonomous car market. This includes Pony.ai, Momenta, and Roadstar.ai. These three companies have started testing its self-driving cars and saw some great progress. However, some of them have a specific commercialization plan while some do not. Some of them fell into the haze while some heading towards the original goal. Pony.ai Based in Fremont, California, and  Guangzhou, Pony.ai has a research and development center in Beijing. Pony.ai was co-founded by James Peng (PENG Jun, 彭军) and LOU Tiancheng (楼天城) in 2016, a former Baidu employee and a former Google programmer. Baidu launched the Institute of Deep Learning (IDL) and started researching on autonomous driving in 2014. Talented programmers from the rest of Baidu’s departments asked to transfer to this team which soon grew from dozens to hundreds of people. However, Baidu is a huge system with lots of departments and regulations, which inevitably led to the company underperforming in terms of speed, innovation, and technology according to an insider. On the other hand, the outside capital market began to realize the valuation of autonomous driving startups and was willing to give support, pulling talents from Baidu to start their own business. Road Testing Pony.ai launched several driverless cars in Nansha district, Guangzhou in Oct 2017. Pony secured approval to offer autonomous car rides to public members within a 1.7 mile-route in Guangzhou in February 2018. Partnered with a Guangzhou carmaker, Auto Group, Pony.ai  launched China’s first public fleet of autonomous vehicles in Guangzhou in May 2018. The company claimed that it planned to increase self-driving cars offering ride-hailing services in the district to 50. Apart from progress in Guangzhou, Pony is the second company to be granted a T3 license—the highest-level issued in the country so far—after Baidu, which obtained one in March 2018 (Plates have five levels, from T1 to T5). Baidu and Pony.ai had been working together to persuade the Beijing government to issue road testing licenses before the company was granted one, according to people familiar with this matter. Pony has launched a WeChat mini-program, allowing users in Guangzhou to hail autonomous taxis since Jan. Currently, only Pony.ai’s employees and a few referral users can use the app (see more in this article). Users can hail self-driving taxis from a designated location to Pony’s offices and other locations set previously by the company. It is quite a big step forward than a simple driverless car testing exhibition. "It would be easier to conduct road testing under closed circumstances without influence and disturbance from other cars," according to people familiar with the matter. Pony claimed that it hopes to grow its vehicle fleet from 20 to 100 in 2019 which will increase its data collection capabilities. The company needs to improve basic technological infrastructure to sustain fleets running with high stability performance. Possessing a perfect model equipped with L4 technology is not enough for a company to prove that its technology is capable of mass production or should be considered for the possibility of reproduction, according to Pony. Competition is brewing and there is enough reason for an early launch to achieve the first-mover advantage in the autonomous car market. But it must take three steps: testing earlier generation prototype vehicles to small-scale fleet testing and finally large-scale mass production with commercialization, according to Pony. Details mentioned above show that Pony is currently deploying small-scale fleets and improving rapidly to support a commercial launch in the future. Algorithms Pony debuted its latest full-stack self-driving solution named PonyAlpha in September 2018. The system features improvements across hardware and software. In terms of hardware, the new system includes increased sensor coverage provided by additional Lidars, radars, and cameras. In order to support these additional sensors, PonyAlpha also features a more highly optimized hardware platform, resulting in a tightly integrated full-stack system, the company claims. The company has a multi-sensor approach to blending separate data from multiple sensors—lidar, radar, and camera—together. Its latest configurations comprise of three lidars, six cameras, and an MMW radar, according to the company. When combined together, the full sensor suite can see roughly 200 meters. This enables a wider variety of driving scenarios.  In addition, Pony’s team released its Vehicle Control Center and its in-vehicle interface PonyHI (Pony Human Interface). The Pony.ai Vehicle Control Center enables centralized management, tracking, and dispatching of all vehicles in its fleet. Passengers are able to follow the vehicle’s every move and decision as it identifies, classifies, and plans its next move based on its surroundings through PonyHI. As we mentioned before, the company conducted R&D and road testing in China and the US. The company uses the same basic system in different countries,  adjusting only for driving habits, traffic rules, pedestrian habits differences, etc. Too difficult to fulfill A mission? Pony tends to make predictions on future trends to get itself prepared. For instance, the company reserved space for its software, hardware, and internet control to accommodate for new technologies like the 5G and V2X (Vehicles to Everything) and regulation requests. The Beijing government asked granted companies to install monitor devices on their vehicles to upload collected data, according to iyiou.com. The company’s ambition is to be an individualized L4 autonomous driving solution provider, which we can tell from its backer and products plan. Momenta has received Daimler's investment, and Roadstar has released a clear plan that it will launch L2/L3 autonomous driving solutions for OEMs. Roadstar.ai Founded in 2017, the company launched an R&D center in Shenzhen and Silicon Valley. Using a similar sensor fusion model, the company focuses on providing a solution for L4 autonomous driving. TONG Xianqiao (佟显乔), chief executive officer and co-founder of Roadstar.ai, was a former executive in Apple’s Titan program and Nvidia. Three key persons of the company, including the CEO, CTO HENG Liang (衡量) and now-disgraced (see more in this article) chief scientist ZHOU Guang (周光) all once served on  Baidu’s autonomous driving team. In collaboration with other carmakers, Roadstar aims to produce 200 cars equipped with its self-developed sensor kits and algorithms in 2019. By 2020, it plans to own 1,500 self-driving electric cars to offer ride-hailing services in the core districts of first-tier cities, according to Bloomberg. Similar to Pony, Roadstar plans to offer its own ride-hailing application for certain users this year, but we’ve never been aware of any news about it yet. Even though the company has said that their products will be designed for passenger cars, it pointed out that it will consider offering self-driving truck solutions for OEMs. Roadstar announced it completed USD 128 million round in May 2018 and launched an "Aries·Rui", an all-weather Level 4 self-driving system using locally-produced lidar, which costs less than CNY 300,000 (USD 44,681.42) at that time. It will further decline to CNY 50,000 (USD 7,446.90) as the price of lidar will decrease in 2020. The company also claimed to have developed an in-house simulation solution, a pair of sensor fusion technologies called HeteroSync and DeepFusion. It’s also known as an "HD RealityMaps" product touted as "providing a 360-degree VR-like vision". Roadstar claimed more than one million miles of public road testing had taken place as of November 2018. The company touted its latest "modular" Level 4 system called "Leo·Ling". The company claimed that "it’s close to a mature commercial product" at a press conference in November. Momenta We’ve written an analysis article about the company’s struggle in commercialization before, outlining its strong academic atmosphere,  strategic investors, and state-owned background investors. We also introduced its wide range of products which comprises of post-installed ADAS (Advanced Drive Assitance System), L3 level factory-installed autonomous driving solutions for highways and urban loops, L4 level factory-installed autopilot solution for autonomous parking, and autopilot solution for urban roads. Unlike Pony, Roadstar has a clear commercialization plan. But it is complicated and has a wide range of products such as offering L2/L3 solutions for OEMs. Therefore, we hesitate to define the company as a pure L4 autonomous driving solutions provider. TONG once implied that the company’s advantage was its high-definition map system which can turn raw data into data that has decimeter precision in an interview. Roadstar also planned to explore vertical scenarios such as food delivery that's partnered with Meituan, the company announced in July 2018. Roadstar and Momenta seemed to be distracted by other opportunities in the autonomous driving industry. “Focusing on too much stuff can make startups less concentrated,” an insider told us. A misleading story told by DMV? California’s Department of Motor Vehicles released the latest batch of reports from companies testing self-driving vehicles in the state last month. This sheds some light on the process and level of road testing. We look at the number of test miles and miles per disengagement in California in 2018. Disengagement covers instances where humans manually disengage the car’s self-driving mode or when the autonomous driving function fails and automatically disengages. This vague definition published by DMV, however, has allowed AV companies to avoid reporting certain events that, depending on how you look at it, could qualify as a disengagement, according to Sebastian Gogola. Also, there is no bias for comparison: different scenarios or time are not considered in this report, which means the numbers you see cannot tell a full story about the company’s technology. Nevertheless, let’s look at the numbers. Waymo drivers disengaged the auto-drive function once every 11,154 miles and Cruise drivers once every 5,204 miles on average. Pony is the front-runner among Chinese autonomous driving companies. The company reported human intervention once every 1,022 miles, according to the report. Baidu was the runner-up among Chinese competitors in the tests, reporting human intervention once every 205 miles on average in its self-driving cars. Baidu logged 18,093 miles in the report. “It’s better to treat the reported miles and index as a guide and not as the single criteria to judge those companies. We suppose the company with a much higher MPD (Miles per Disengagement) and reported miles did a better job. For example, Waymo and GM Cruise both posted significantly more miles driven and lower disengagement rates than  other companies. Therefore,  we can say they are the best. However, we always need to remember that the total miles and the average number may be deceiving,” an insider of the industry told EqualOcean. Rebalancing power in L4 Era The traditional auto industry’s pyramid structure put OEMs on the top, followed by various suppliers with limited power. In the L4 era, however, the dominance of OEMs will face challenges as auto part suppliers, internet giants, algorithm companies, chip manufacturers, and sensor suppliers have stepped up efforts on R&D and related commercial applications of autonomous driving, according to a report published by Deloitte.  “Ride-hailing companies like Uber and DiDi have access to first-hand customer data and they have interfaces to operate cars. That’s where their advantages lie in this L4 era. Autonomous driving companies could serve as their technology supplier. Actually, tier 1 suppliers, map providers, Lidar manufacturers, algorithms companies, OEMs, and ride-hailing providers all have opportunities to bring fundamental changes to the competitive landscape and earn bargaining power,” an insider in this industry told us. The Alphabet’s subsidiary, Waymo, announced it will sell its custom Lidar to companies outside of self-driving cars last week. Waymo has been caught in a dilemma: it was valued at USD 175 billion by Morgan Stanley but has difficulties in commercialization. Selling Lidar is still in its trial phase as it builds a reliable engineering delivery team. However, it isn't helpful in its autonomous driving business. March 13, a consortium that includes SoftBank Group is in its late-stage talks to invest USD 1 billion or more in Uber Technologies Inc.’s self-driving vehicle unit, reported by the Wall Street Journal.

Analysis EO
Analysis · 2
Analysis EO
Jan 18, 2019 06:07 pm ·

60 Autonomous Vehicles from 9 Companies Are allowed on Beijing’s Road

The Beijing-based autonomous vehicle making startup Idriverplus (智行者) has recently been granted 2 license plates that empower it to test autonomous vehicles on Beijing's public roads, making the company the 9th one allowed to do so, according to the Auto Business Review. Thus there are 60 vehicles from 9 companies are allowed to run driving tests on Beijing's public roads, the 9 companies include Baidu, NIO, BAIC BJEV, Daimler China, Pony.ai, Tencent, Didi Chuxing, Audi China, and Idriverplus. Founded in 2015, Idriverplus focuses on developing low-speed autonomous vehicles that can be deployed in urban sanitation, logistics, and security fields. Apparently, the company regards low-speed vehicles as an easier way to achieve autonomous driving. For example. the company's autonomous street sweeper Woxiaobai (蜗小白) will be mass-produced in this October. As the political center of China, Beijing is especially strict about public safety in the city. Companies have to obtain special license plates for each of its autonomous vehicles to run on the city's public roads. And it's never easy. Companies that want to run public tests on Beijing's road have to test their autonomous vehicles in a closed designated area for over 5,000 km in order to gain the qualification of taking a road test. During the road test, the autonomous vehicles have to make movements including speeding, braking, wheeling, and changing lanes under the autonomous mode, without violating any traffic regulations. And there will be recognized third-party organizations reviewing whether the human drivers of each autonomous vehicles are ready to take over driving actions when danger, according to companies that have participated in the test. And the plate only valid for 6 months, companies have to take the same test after that. The extremely strict regulations have made Beijing the hardest city in China to gain autonomous tickets. There are only 60 autonomous vehicles in the city are allowed to drive in public road, with 45 of them belonging to Baidu, the search engine giant in China. With the license plate, autonomous vehicles are granted access to 44 roads in Beijing, adding up to 123 km, covering 85% of all the traffic scenarios, and extending to highways, urban area, and the countryside. Baidu became the first company ever to be granted the license plate on 22 March 2018. Now there are 9 companies and 60 vehicles are granted the plate, while Baidu owns 75% of them. Nationwide, there are 22 companies in China have been granted plates to run public tests. Among them, 62% of the companies are traditional automakers, 17% are tech companies like Baidu and Tencent, and 13% of them are autonomous driving algorithm startups like pony.ai and Roadstar.ai, according to iyiou.com. Many Chinese companies have chosen California as their road testing destination due to China’s strict regulations. There were 60 companies were granted the Californian autonomous vehicle plates, and 20% of which are Chinese companies. It’s conceivable because most Chinese AI algorithm and autonomous vehicle-making startups are both headquartered in China and the United States, especially in Silicon Valley. But the most important factor is that California provides a much easier way for companies to gain an autonomous plate. The state requires companies to provide insurance or a bond of USD 5 million and show that its cars (1) complied with federal regulations, (2) could navigate roads, and (3) were tested in areas of operations. The companies have also to submit a safety plan outlining how they would interact law enforcement to the the California Department of Motor Vehicles (DMV). Only by doing this will an autonomous vehicle license be issued. Companies can even run a fully driverless vehicle test on California’s roads, Waymo became the first company to obtain a fully driverless testing permit from the DMV in October 2018. Unlike previous tests, the autonomous vehicles that are tested won’t have a safety driver, only steering wheels, brake pedals and other manual controls specified by the National Highway Traffic Safety Administration, according to Venture Beat. But its impossible to run a fully driverless test in China, at least for now. Beijing has categorized its autonomous vehicle public roads testing into 5 grades, from T1 to T5 depending on difficulty. And the city has only granted T1 to T3 licenses to applicants. The city plans to build an area for T4 and T5 autonomous driving test in the middle of 2019, allowing companies to test fully driverless vehicles.  It’s conceivable that the T4 and T5 license plates will be more difficult to obtain, but autonomous vehicle companies have to do it anyway. Because the road structure differs from city to city in China. For example, the standard driving lane width in Beijing is from 3.75 meters to 4 meters, while in Shanghai the width is averagely 3.25 meters. The difference might sound nothing to human drivers, but has a significant impact on the self-driving system. Not meaning that the Beijing plate is more important than Shanghai or Shenzhen plate, but that autonomous vehicles companies have to run tests in Beijing in order to adapt its special roads structure, as Beijing is one of the biggest auto markets in China.

Analysis EO
Analysis · 2
Analysis EO
Nov 12, 2018 04:06 pm ·

Genius team of Pony.ai was a blessing might also be a curse

Business pioneers lead the trend and cultivate talents as well. Global autonomous driving leaders, like Waymo, Tesla, Mobileye, etc., have exported numerous talents to the industry, globally wide for certain. Baidu is seemed to play a similar role in China with the above global pioneers. In order to figure out how this Chinese Internet Tycoon had helped the development of the autonomous driving industry of China (click to check the original article on our Chinese website), EqualOcean used to make a name list of those senior autonomous driving talents “escaped” from Baidu. It showed that 28 tech seniors left Baidu and then established 18 driverless technology startups because of the organization rigidity of this large enterprise, or maybe the great temptation from the vigorous venture capital market. They believe the market will be huge enough to breed a series of participants in the industrial chain. Among the above mentioned 28 tech seniors/seniors, PENG Jun (彭军) and LOU Tiancheng (楼天城) co-founded Pony.ai in December 2016. The company has already made relatively remarkable achievements so far. It was the first unicorn company among all driverless technology startups in China, and hitherto has raised a total funding amount of USD 235 million and had a market valuation of USD 1 billion. Pony.ai is on par with Baidu, with Beijing T3 plate What can prove the actual capability of Pony.ai would be the obtainment of the T3 plate. It is commonly accepted that the T3 plate has the strictest admission control all over the world, and is the highest level of road test plate issued in China so far. T3 plate requires the test vehicles to have a comprehensive series of abilities, such as road condition cognition, traffic law compliance, planned route execution and emergency response On July 2, 2018, the company announced that it had secured the T3 autonomous vehicle testing plate in Beijing, making itself the first startup on the list, right after Baidu. And in fact, Pony.ai and Baidu are the only two companies in China that have secured the T3 plate so far. Just like the human driver plate, to obtain the T3 autonomous plate needs to pass several examinations (listed in the chart below), and before the road test, T3 plate demands the vehicle to finish a 5000-km training in a certain blocked training ground.  Pony.ai passed all these series of tests, within only 10 days. PonyBrain, a whole new platform other than ROS Pony.ai’s technical route has its own characteristics. A truth is, “You go get a couple of graduate students together, you get a car, you download ROS (Robot Operating System), and you can probably get a self-driving car driving around a parking lot within six months”, said Chris Urmson, the former head of Google’s self-driving car program, in an interview with The Atlantic. Other than selecting ROS, Pony.ai chose another technical route - they tried to build a whole new platform, an autonomous driving system as well as the operating platform, named PonyBrain. Because the team believed ROS was not the best suitable solution for the autonomous vehicles, and they claimed that PonyBrain had its own advantages, like supporting fast iteration, better adaption to the onboard computing environment, and improving real-time performance, job scheduling and data transmission capabilities. As a result, “PonyBrain is 20 times faster than the traditional ROS”, PENG Jun said in an interview with Xin Zhi Jia before. PonyAlpha, the first ever product-ready system According to Pony.ai’s news published on their official website in Sep. 2018, the company debuted its latest self-driving system: PonyAlpha, which was claimed to be the country’s first-ever product-ready autonomous vehicle system, a full-stack self-driving solution that had achieved levels of stability and performance capable of sustaining a consumer ride-hailing fleet. An autonomous fleet equipped with PonyAlpha was also invited to the 2018 WAIC (World Artificial Intelligence Conference), bringing people a trial ride of the driverless vehicle during the event. Before the WAIC, this fleet actually existed in Guangzhou since February this year, and Pony.ai was chosen to be the autonomous driving technical partner, by GAC Group (Guangzhou Automobile Group Co., Ltd.), one of the leading auto companies in China. Furthermore, with the help of GAC, Pony.ai chose robotaxi as its entry point of business, aimed to the build a fleet of 100 driverless vehicles, and planned to launch a production driverless vehicle in 3 years. Pony.ai’s autonomous fleet in Guangzhou The genius team is the company's core competence, while a blessing might be a curse Pony.ai has a super brain core team Pony.ai’s co-founder & CEO, PENG Jun was the former chief system architect of Baidu autonomous driving department (T11 – the highest technical position level in Baidu company) and took responsibility for leading the overall strategy and development. Another co-founder & CTO, LOU Tiancheng, was the youngest T10 engineer in Baidu history, the chairman of the autonomous vehicle technical committee, and the recognized leader in computer programming. LOU was also known in the programming world as "Bishop LOU”, and had been famous since high school. His career began in Google X, the predecessor of Waymo, with a focus on the development of driverless vehicle technology. YAO Qizhi (姚期智), the academician of Tsinghua University, the only Chinese winner of the Turing Award, is now Pony.ai’s chief consultant, who was also LOU Tiancheng’s professor at the University. HU Wen (胡闻), the former TMT investment department leader of ICBC international, works in Pony.ai as the COO. PENG Jun & LOU Tiancheng However, like the Chinese ancient philosopher Lao-Tzu once wrote, “a blessing might be a curse”, there might be some risks lurking in this super genius team. PENG is the CEO of the company, who showed up in all kinds of public activities, as the speaker and the leader of the team and company, while the CTO “Bishop” LOU, with his low profile, seemed to be the real soul of the company, and used to be much more famous than PENG. A company’s competitiveness ultimately depends on the strength of its team, which will be highly determined by the organization mode. How the chemistry works among this genius team members might be important for Pony.ai to discover. - Author: ZHANG Fan; AN An contributed to this article. Write to ZHANG Fan at ZhangFan@EqualOcean.com