Subscribe

Analysis EO
Analysis · 2
report
Analysis EO
Jun 23, 2020 11:00 am ·

TuSimple: Start with the Low-Hanging Fruit

► Though the market size is relatively small, with an explicit application scene, an affordable technology, and a sound regulatory system, the autonomous truck is very likely to realize commercialization sooner than other self-driving vehicles. ► TuSimple aims to bring the most cost-effective autonomous truck solution to both Chinese and American shippers and logistics companies. ► The company’s backbone technology is a proprietary automotive-grade camera system co-developed with Sony Semiconductor, which unlike Waymo’s lidar-focus solutions, utilized customized HD cameras that are much cheaper than lidar. ► The dual-site deployment and the obsession with cost reduction put the company one step ahead of the commercialization process. A big opportunity in a small market IHS Automotive, the American automotive research firm, divides application scenarios for autonomous vehicles into four categories: last-mile delivery, autonomous trucks, fixed-route and Robotaxi. While the four businesses have the same core technology, they are very different in the commercialization process. Robotaxi has been the hottest in recent years. Many giant companies like Google, GM Cruise and Baidu are implementing their own Robotaxi fleets. Autonomous trucks, on the other hand, doesn’t share the same buoyancy. The once ambitious self-driving truck start-ups, including Otto and Starsky Robotics, were either acquired or declared bankruptcy. Even those who survived still don’t get as much support as Robotaxi operators do. Take for example Pony.ai and TuSimple, the two self-driving solution developers who both run their vehicles in both China and the US. Only the former has implemented Robotaxi, while the latter focuses on freight. While Pony.ai is still in the early rounds, its total funding exceeded TuSimple’s twice over. The vast gap could be the result of the dominant potential demand Robotaxi owns. According to Mobileye’s research, the market size of Robotaxi will be worth more than USD 16 billion by 2030, while the number by then will be USD 1,550 million for self-driving trucks, according to MarketsandMarkets.  But the size isn’t everything. While the self-driving taxi is expected to save operation companies 70%-80% of profit currently taken by the human drivers, according to WeRide’s research, in this stage, with the immature technology and stubbornly high R&D costs, the goal is not likely to be achieved soon.  Autonomous trucks, on the other hand, require easier technology and less costs. Since the trucks transport goods rather than humans, and run on highways mostly, they have lower bars on sensitivity and speed control, and thus cost way less than developing a self-driving passenger vehicle. Besides, according to the US Department of Labor, the median annual income for heavy truck drivers in the US is USD 42,480, surpassing the median annual income of USD 37,690 for all occupations in the nation. Two drivers are required in one truck during one trip. The autonomous truck can at least take one driver off the truck to save 50% of the expense.  Furthermore, fatigue driving problems still exist in freight companies. A report from the US Department of Transportation showed that, in 2016, fatal accidents related to heavy trucks in the US numbered 3,864, which is 11.2% of all vehicle fatalities during the year. Autonomous technology, however, will enable trucks to run safely for 24 hours nonstop, even in extreme weather, which can highly promote the efficiency and safety of freight while saving money.   The lower cost and tangible implementation have given the autonomous truck a great opportunity to achieve commercialization sooner than Robotaxis will. TuSimple intends to further speed up the process Founded in 2016, TuSimple, the Beijing and San Diego co-based autonomous driving company has been focusing on bringing the most cost-effective L4 autonomous driving solution for long-haul heavy trucks in both China and America. And it is leading on that track. Over the last five years, the company has secured a total of USD 298 million fundraising led by renowned capitals including Sina, Mando Corporation, CDH Investments and UPS, with a post-money valuation of over USD 1 billion.  In contrast with Tesla, which has again delayed its self-driving trucks’ time of delivery to 2021, TuSimple is the first and only to operate a fleet of autonomous heavy-duty semi-trailer trucks on pre-mapped shipping routes. It has provided door-to-door services to a total of more than 20 clients in the US, including UPS Express, Amazon and United States Postal Service (USPS). Three shipping terminals are implemented in Arizona, Tucson and Texas with three more under construction. The company is also running road tests in Fujian and Shanghai, China, even though the commercialization may come later due to regulatory restrictions. The Sina-backed company claimed that revenue reached USD 1 million per month in the second half of 2019. Still, it would only become profitable when the safety officers are no longer needed, and the company intends to go public at that time. The secret is the obsession for cost control One of the most significant reasons for TuSimple’s fast commercialization is its obsession with cost control. As known to all, lidar is an indispensable part of autonomous vehicles due to its excellent performance in ranging and resolution. It is the best solution for driverless cars to cope with complex road conditions. But the sensor, once exclusive to the military, also costs a considerable amount. The US lidar giant Velodyne sells its 64-channel lidar units for USD 80,000 each.  Faced with such a huge expense, TuSimple began to reflect on whether it is really necessary. And apparently, it’s not. Since the trucks spend 90% of operating hours on highways where they should face obstacle-free movement, the company realized that its truck doesn’t have to be so sensitive, but it has to see further than lidar because its long body requires a much longer safe braking distance. Unable to find a system on the market that fits its needs, the company leaders decided to create one by themselves. Partnered with Sony Semiconductor, TuSimple successfully produced its proprietary automotive-grade camera and vision system that can see 1000 meters away at night and in extreme weather, in May 2019. And most importantly, since the system is mostly based on HD cameras, it costs much less than lidar-oriented solutions. This integrated system started mass production since this April, jointly with ZF, the renowned German automotive supplier.  In addition, the company also did a good job on energy saving. A joint study by TuSimple and the University of California, San Diego shows that automated trucks can save up to 10% on fuel consumption with TuSimple’s self-driving system, and estimates that, if all medium-and-heavy-duty trucks in the US were equipped with the same system, USD 10 billion annual fuel cost would be saved. With a sound technology foundation, the company also benefited a lot from its duel-site operation strategy. While their cross-city trunk line shipping has already been commercialized in the US, they are also actively road testing in harbor roads and logistics parks in Shanghai,  to collect as much operation data from different road conditions as possible. The system is now refined by nearly 2000-hour and 30,000 miles of real-world road tests.  

Analysis EO
Analysis · 2
report
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!   

Analysis EO
Analysis · 2
report
Analysis EO
Oct 12, 2019 12:00 pm ·

Interview with Chen Mo: Mass Production Comes as Early as 2023

Founded in September 2015 and based in Shanghai and San Diego, TuSimple (图森未来) is one of the first batch of companies in China to develop Level-4 (SAE-standard) commercial autonomous driving truck solutions. Since August last year, the company has started small-scale commercial operations on Highway 10 in Arizona; it has been engaged by some large retailers and e-commerce platforms. According to the company, TuSimple has about 50 driverless trucks in China and the US. Each truck can generate thousands of US dollars a week at present by transferring goods. In the case of meeting research and development needs, the remaining vehicles are headed to the road for trial operation. However, the business is not profitable because each vehicle is equipped with a security officer and a tester who are both paid. Moreover, R&D costs and the salary of more than 500 employees amount to a sum much higher than the freight income. In an interview with EqualOcean recently, Chen Mo (陈默), the founder and CEO of the company, said that the profit could come soon in 2020 when the company was able to remove the people. By then, the company could break even in 2023 or 2024 once its self-driving vehicles reach mass production in two to three years. However, he also mentioned that the worst case might be that the autonomous driving startup breaks even in 2027. Bet on the US market Unlike many Chinese companies that have placed centers in China, TuSimple is more aggressive in developing its business in the US. There are very rational business considerations behind this. Chen Mo told EqualOcean that one of the reasons was higher overseas labor costs. According to the US Department of Labor, in May 2017 the median annual revenue of heavy-duty truck and large trailer drivers in the US was USD 42,480, while the corresponding annual income of Chinese drivers was about USD 14,028. While the cost of employing drivers is increasing, there is also a shortage of truck drivers. According to Bob Costello, chief economist at the American Truck Association, the number of drivers in the trucking industry reached 50,000 by the end of 2017. If the trend continues, the shortage of truck drivers may amount to more than 174,000 by 2026, which will inevitably lead to higher employment costs. In addition to the cost of employment, relatively sound autonomous driving laws and regulations are one of the issues that prompts TuSimple to actively expand in the US market. According to Chen Mo, there are 16 states in the US where autonomous driving companies can conduct autonomous driving road tests and four states where companies can conduct commercial trial operations. It is expected that, by the end of 2020, all states in the nation will open autonomous driving road tests. In contrast to China, the US has better commercialization scenarios, and any Chinese autonomous driving company that does not launch a commercialization process overseas will not be able to fulfill large-scale commercialization in China. The quality and cost of trucks also prompted TuSimple to increase its bet on the US market. Relatively speaking, China's OEMs are less mature than those in Europe and the US. For instance, they have backward development of line control systems. For the same line-controlled truck, the cost is cheaper abroad. Achieve mass production as early as 2023 TuSimple's current business model is simply to use driverless trucks to help people transport goods and dig deep in the logistics field. Chen Mo said that TuSimple directly cooperated with OEMs in China and the US and shared revenues. In other regions, such as Japan, Korea, Australia and Europe – where the company does not operate business currently – TuSimple may choose to rent out self-driving technology and charge license rental fees. At present, TuSimple has 18 partners, including shippers and OEMs. Most of TuSimple’s clients are taking its autonomous trucks on trial currently, and large-scale applications of self-driving trucks will emerge after the vehicle costs become lower than the existing labor costs. Only after the software is ready will clients choose to place orders – by then OEMs will be willing to do mass production, as it takes a long period and large investment to prepare hardware parts. Chen Mo expects that TuSimple's self-driving trucks will drive out and start receiving orders in 2021 and achieve mass production in 2023 or 2024 when costs begin declining. Since the company's inception, TuSimple has received a total of USD 298 million in financing. Investors include Sina, NVIDIA, Compound Capital, ZP Capital, CDH Capital, UPS and Mando Corporation. Among them, Compound Capital, ZP Capital and CDH Capital are financial investors. On September 17, TuSimple completed an extended Series D round and received USD 200 million in funding. Chen Mo told EqualOcean that the company started the D round of financing in October last year, but due to the deterioration of the economic environment and downturn of the capital market, it finally completed the financing in September, which took much longer than expected. When it comes to introducing investors, Chen Mo said that TuSimple welcomed equity investment from upstream and downstream industries. For example, the US logistics company UPS and the Korean automotive Tier 1 supplier Mando Corporation were introduced in Series D. At the same time, he said that the news that Amazon was in talks to acquire TuSimple was fake news, but the two companies did have a conversation in terms of investment. Although the environment of China's capital market is not good, TuSimple seems to have no capital concerns. When talking about most Chinese Level-4 companies having to turn to the lower level (Level 2 and Level 3) development due to funding problems, Chen Mo said that TuSimple did not consider the Level 2 and Level 3 markets at all, because the company's advantages lie in the Level 4 software algorithm and lower level of autonomous driving can emphasize hardware parts more compared to Level 4. He said, “You cannot just do Level 2 or Level 3 algorithm alone.” However, he also admitted the biggest problem the company may potentially confront may be the company’s lack of cash flow to support driving out, which is a common problem for all Chinese autonomous driving companies. Target Waymoo and Daimler In China, the competition in self-driving truck industry is becoming fierce. When talking about competition, Chen Mo said: “After 2015, the window period of the autonomous driving truck field has gradually passed, and new startups cannot have enough resources to survive.” In the eyes of Chen Mo, in the market and technical strength considerations, his opponents are just Waymoo and Daimler. The former ported its mature passenger car driverless system to the truck. In March last year, it began to use the self-driving truck to transport goods in Atlanta's data center, using the same set of custom sensors as its passenger car. The traditional car companies represented by the latter are also stepping up to make driverless trucks. Chen Mo is a serial entrepreneur. Before creating TuSimple, he founded a number of companies covering the fields of advertising, gaming and used car transactions. Talking about the difficulties of cross-industry entrepreneurship, he said: “The main thing is to master the essence of doing business: learn to match resources and formulate strategies.”

Analysis
Analysis · 1
Analysis
Analysis · 1
Analysis EO
Analysis · 2
report
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
Nov 12, 2018 12:00 pm ·

Uber quit, will TuSimple succeed?

It’s been no more than ten months since Waymo, the self-driving car spinoff of Google parent company Alphabet, launched a robot truck project in Atlanta with Google, and Tesla Semi trucks were spotted on their first cargo trip. Not even mention the increasing number of companies bet on the future of commercial truck including Daimler, General Motors and Tesla, etc. The ride-hailing behemoth Uber's efforts in autonomous truck have been impaired a lot due to the legal fight with Waymo in 2016, and whose plan back on trucking on March 2018 and ended four months later. The race to manufacture self-driving trucks just got a little less crowded. Getting started: Why autonomous heavy truck? Chen Mo, co-founder and chief executive officer of TuSimple started his business simultaneously in both San Diego and Beijing three years ago, as both America and China feature a trucker shortage and huge cargo transportation demand. He is not the only one who sees the opportunity, obviously. The competition on purpose-designed autonomous technology is extremely fierce. There are three main kinds of major players including tech giants, OEMs and startups. First, tech giants like Google, Baidu, Uber, and Tesla, who have large groups of customer, which led to their first and biggest vision is either to create an autonomous passenger car or providing ridesharing service, or (if there is something about logistics) commercial delivery applications dedicated in "last-mile". Second, OEMs, like Daimler, General Motors, and Toyota, you may hear them about their self-driving vehicles for on-demand robotaxi services, few OEMs will jump into the rival of autonomous truck owing to large operational costs and high risk. Last, the startups, let’s cut them into two sides. One is deeply related to Google and Baidu, for example, Pony.ai, whose CEO James Peng —the former chief architect of Baidu (which itself is making big inroads into car system). As we discussed before, it is not hard to draw to the conclusion that this companies’ missions are very much like Google and Baidu—autonomous passenger car. And finally, let’s draw our attention back to TuSimple, belonging to the other side of startups, who focus on commercial trucking at first. The race there is relatively mild and they can get a shot at taking on the industry leader. Two recent shreds of evidence reported by Forbes shows that TuSimple seems to reap the first-mover advantages on the long-haul autonomous truck technology. On 31 August 2018, the company announced its a much-needed breakthrough with a vision system that lets trucks see and track vehicles on the highway up to 1,000 meters ahead, which any other system fail to achieve. And the story is not just about distance. On 1 Nov, TuSimple released a video that showed one of its truck operating smoothly on public roads outside Tucson during an apparent heavy downpour without ANY intervention by the technicians in the truck, as the truck contended with wet roads or stopped at a rail crossing to wait for a freight train to pass. No wonder why they think it may have an edge on Waymo when it comes to driving in heavy rain. Camera, other than Lidar Discuss on Lidar has a long history. In 2016, Tesla claimed it chose not to use Lidar as one of the sensors on its cars to assist with its autonomous features, instead, Google used Lidar as one of its dominant sensors and insisted it's necessary. Lidar is an acronym of Light Detection And Ranging, (sometimes Light Imaging, Detection, And Ranging) and was originally created as a portmanteau of “light” and “radar.” Lidar works well in all light conditions but has a difficulty dealing with extreme weather. The higher range and resolution the Lidar system is, the more it costs, which is often thousands of dollars and obstructs startups from a mass experiment. Today, some companies have already somehow managed the problem. Hou Xiaodi is the co-founder, chief technology officer, U.S. unit president of TuSimple and a Caltech-trained cognitive neuroscientist. It is distinctive that typical tech startups deploying artificial intelligence to drive vehicles whose founder or CTO is commonly computer-science-backgrounded. Hou found a greater reliance on high-definition camera than Lidar under extreme circumstances and trained perception system to identify objects properly. Aside from that, Hou uses deep learning a lot, consuming a huge amount of computer resources. Moreover, a conjugate algorithm is needed to reach a stable result, which will require more computation. It turns out that there will be a lot of graphic cards used in the automotive chassis. Nvidia makes all this happen. “Based on the triumph of deep learning, the majority of our sensors are camera based. With the camera-based sensors, we can actually beat the performance of some of the Lidar performance, using them as pure cameras.” Said Hou. Practices, and more practices The company is doing a lot of research and road testing in China and America. According to China Knowledge, they secured a road test license from both the California Department of Motor Vehicles in June last year and Shanghai officials on 19 October 2018. They also marked 100 days of safe operations of its autonomous driving trucks in July this year. The trucks had transported 5,000 fully-loaded containers. It’s essential for the company to gather and pick up information when they operate road-test to perfect the autonomous system. ——Author: LinYan.Write to LinYan at LinYan@EqualOcean.com