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In this column, we look at:

1. Potential Market for Self-driving Cars

2. Current Autonomous Player Landscape

3. Self-driving Tech Stack

4. Technical and Ethical Challenges in AV Development

5. Implications for the Future

For years, self-driving cars, the $60 billion business opportunity , has kept the industry analysts and investors humming about the potential of a ‘new mobility revolution.’ But two months into 2020, automotive players and tech firms are yet to deliver on that promise. Additionally, there was a much-talked up trillion-dollar market opportunity in 2017 with Intel predicting the economic opportunity will grow from $800 billion to $7 trillion by 2050. There’s another segment which is booming — Advanced Driver Assistance Systems (ADAS) market, dominated by Tesla. This market is expected to touch $81.14 billion by 2025, data from Adroit Market Research indicates. Meanwhile, there are no signs of a slowdown in the automotive startup funding sphere, with overall startup funding increasing 10x over the last five years, data from Crunchbase indicates. So does an autonomous future still exist? Despite the aggressive oversell and the herd-like push for autonomous cars, Level 4 or 5 autonomy still sounds like a moonshot. Even if they land within the next few years, the shiny autonomous vehicle s will be best for a geo-fenced experience, industry insiders believe. Or better yet — drive low-speed, perhaps < 25 mph on well-known routes with lower complexity. This is exactly the use case Michigan-headquartered startup May Mobility is targeting.

According to May Mobility CEO Edwin Olson, who gave a great reality check on self-driving cars in a post, self-driving cars are 0.01% as good as humans. “Even with performance doubling every 16 months, it will take 16 years to reach human levels of performance — that’s 2035. This makes the claims that AVs are coming in 2019 or 2020 sound pretty dubious,” he shared in the post.

Learn More: Four Convergent Technologies That Make Fully Autonomous Vehicles Possible

Since 2013, when Google’s self-driving cars sprung up in the public domain, the boundaries have blurred significantly between traditional OEMs, big tech firms, automotive equipment suppliers, and autonomous startups. The automotive future is now helmed by hardware and software makers such as Waymo, Uber, Lyft, Cruise Automation, Otto car makers and carmakers like Tesla, Ford, GM, Toyota and Audi, who are coalescing behind path-breaking self-driving startups to catch up. There’s a new crop of startups fueling the autonomous AI value chain with path-breaking solutions such as lidars and sensors, ADAS and connected cars safety solutions and autonomous vehicle simulation software. The disruptors, such as Drive.ai, Mobileye, DeepMap, Velodyne, Nuro , and Luminar Technologies build everything from high performing custom hardware, lidars to data and mobility management solutions. In fact, Intel-owned Mobileye, a leading vision chip supplier for autonomous driving has evolved into a vendor to the industry-at-large with BMW, and Ford counting on its solutions.

There’s another key trend cropping up — Big Auto is building a moat to stave off competition from tech firms. The term partnership ecosystem plays out perfectly in the automotive industry. In 2017, automakers Ford-Volkswagen joined hands to invest in storied self-driving startup Argo AI, valued at more than $7 billion. The partnership aimed to create efficiencies for both the players and allowed them to “integrate Argo AI’s self-driving system into their own vehicles, delivering significant global scale.” Meanwhile, German automakers BMW and Daimler formed a one-of-its-kind $1.13 billion mobility partnership to rival self-driving tech giants. Big Auto has also firmed up its position by snapping up autonomous startups — notable mentions include GM acquired Strobe Inc and Cruise Automation and Ford’s investment in Argo AI. Partnerships are bound to grow with analysts expecting traditional players coalescing into networked ecosystems, teaming up to challenge Big Tech and disruptors.

Learn More: Nvidia Goes All In on Autonomous Tech, Unveils Next-generation SoC Orin

According to Jensen Huang, founder and CEO of NVIDIA, “Creating a safe autonomous vehicle is perhaps the society’s greatest computing challenge.” In real-situations, where margin of errors are very small, a Level 5 autonomy is very hard to achieve and the complexity of the task requires a scalable, programmable platforms that can handle a large number of applications and at the same time achieve systematic safety standards.

Now, at its very core level, the self-driving tech stack consists of:

Sensors Computing platform Motion planning algorithms and object detection analysis Mapping and location-based services Autonomous Vehicle System Integration

While the core element of the autonomous vehicle technology stack includes sensors, others tools related to path planning, object detection and smarter control algorithms play a huge role in making these machines safe, error-free and deployable on the roads. Tech giants, especially Nvidia and Waymo have leveraged their in-house technological capacities to build end-to-end AI, from the cloud to the car. Autonomous vehicles combine data from all types of sensors (e.g. radars, lidar, sonar, GPS, and cameras) which is processed by using deep neural networks to drum up insights to understand the environment around the car. This is where Nvidia and Intel Mobileye are emerging as clear winners by offering end-to-end platforms for the entire self-driving value chain autonomous vehicles, allowing OEMs and companies to scale from ADAS to Level 5 full autonomy and making deployments a reality. Mobileye, known for its advanced vision technology, also provides crowd-sourced mapping capability while its front-facing camera powers advanced driver-assistance systems (ADAS) deployed in cars.

Learn More: Tesla Slams Door on Nvidia’s Platform for Self-Driving Vehicles

Despite the hype, autonomous space is rife with technical, ethical and logistical challenges. The size and technical complexity of projects has slowed down the road to mass deployment and forced automakers to dial down the rhetoric on full autonomy. Some of the key challenges include collecting, storing and analysing terabytes of data, investment in hardware infrastructure and tackling vulnerabilities in the system.

a) Storing and analysing complex data: One of the key challenges is that autonomous vehicles combine data from a variety of sensors to sense their surroundings. The sensors help machines detect obstacles, read signs and identify the best paths. One of the biggest challenges involves ingesting and managing the data in real time for algorithmic training. Besides the development challenges of collecting and storing huge amounts of data, another challenge includes testing and validation. Real-world environments present a range of complex use cases that can only be solved with testing in unconstrained environments.

b) Validation and testing gap: Despite advancements in semi-autonomous vehicles, there’s still a gap in validation testing. A new research paper, titled Phantom of the ADAS, authored by researchers from Ben-Gurion University showed vehicles can be manipulated with phantom images. “This type of attack is currently not being taken into consideration by the automobile industry. These are not bugs or poor coding errors but fundamental flaws in object detectors that are not trained to distinguish between real and fake objects and use feature matching to detect visual objects,” shared Ben Nassi, lead author of the paper. The vulnerability can be exploited by hackers to manipulate the vehicle and cause the autopilot to apply the brake.

c) Making ethical judgements: Are self-driving cars equipped to make human-level judgement? This and more was the subject of a MIT survey which argued that AVs may soon have to make ethical judgements, once put on the roads. While the subject needs social consensus, the survey also emphasised why AVs should be programmed in a certain way.

d) Steep growth curve: Despite the trillion dollar opportunity and the scope to create value across areas like car insurance, ride-hailing, robo-taxis, and fleet management, industry players need to analyze each business case from a financial standpoint to capture real value.

Learn More: GM Challenges Waymo, Uber’s Supremacy with Driverless Shuttle

As the trend towards putting autonomous vehicles on the road gains momentum, the top question is when will self-driving cars be commercially available. While there’s no credible data to pinpoint when AVs will become a reality in the foreseeable future, autonomous driving will transform urban mobility , and lead to Mobility-as-a-Service business model. Shuttles and robot-taxis are a regular feature on the U.S. roads. Waymo’s robot-taxis are plying on the roads in Phoenix and Silicon Valley while May Mobility’s shuttles operate in select areas in Michigan and Rhode Island. Additionally, there’s no consensus who’s the ultimate winner in this space with players of all stripes are chasing the path to full autonomy. Data from Statista indicates Japanese carmakers Toyota and Honda are still chasing the moonshot.

Here are some of the key trends we’ll see solidifying over the next few years.

1. Companies overstated their capabilities: While the potential for disruption is huge, Level 5 autonomy is still far away into the future. Although, Waymo’s robo-taxis are chipping away at that perception.

2. Collaborations will drive advancements: Over the next few years, close collaborations will continue between car manufacturers and pure play self-driving innovators. Industry analysts believe it won’t be a winner-takes-it-all market like the social media landscape, but a networked ecosystem which will eventually win the long game.

3. Safety is a critical barrier: Despite tremendous R&D being poured into making autonomous vehicles safer on the road, software design and coding errors sometimes lead to testing failures. However, industry insiders reveal the performance of self-driving cars, especially upto Level 2 has improved significantly.

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