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AI

Autonomy is quickly becoming the driving force of the automobile industry. Leading autonomous car makers – GM, Waymo, Tesla, Ford, etc. – are all aggressively improving the infrastructure necessary for autonomy. As there are multiple autonomous vehicle technologies at play, a hot discussion is going on in the market about which one is better – vision based (camera) or sensors based (LiDAR, radar, and ultrasonic).

Currently, pretty much all active players in the market, with the exception of Tesla, are moving forth with LiDAR. Tesla, on the other hand, is betting solely on cameras/vision.

Before we get into thick of the discussion, let’s quickly go through these automotive vehicle technologies (LiDAR, radar, and camera).

LiDAR

LiDAR (Light Detection and Ranging) devices are basically active sensors that emit high-frequency laser signals in quick succession (up to 150k pulses/sec). It measures the time taken for each signal to bounce back and calculate the distance between the vehicle and obstacles with high precision. The main concerns with LiDAR are that it is way too costly and doesn’t recognize colors and traffic signs.

future of autonomous vehicle technology LiDAR

Radar

LiDAR and Radar are essentially the same technologies operating with light waves at different frequencies. Radar emits low-frequency signals; therefore, produce less accurate outcomes (see the image below). While cost is not an issue with radar technology, being a sensor, it also doesn’t recognize colors and signs, which are crucial for autonomous driving.

future of autonomous vehicle technology lidar vs radar

Ultrasonic is another automotive vehicle technology in wide use today. Unlike LiDAR and radar, these sensors use soundwaves to measure the distance from obstacles.

Camera

Being a vision-based device, camera estimates the distance based on its relative size in the captured frame. The main advantage with camera is that it can recognize traffic signs and traffic light colors, which sensors cannot. While cost is another advantage with camera, but given the amount of efforts the goes into training camera-based systems to achieve sensor level accuracy, it is not a very cost-effective solution either.

future of autonomous vehicle technology computer vision

Related read: Computer Vision Trends 2019

Now that we have brushed up the basics of main autonomous vehicle technologies, let’s see how they are faring in the real world.

Current Industry State

It is clear that one technology – be it vision or sensors – cannot provide the ultimate solution for driverless cars. As a senior analyst at Navigant puts in “Layering sensors with different capabilities, rather than just relying on a purely vision-based system, is ultimately a safer and more robust solution”.

So, the question here is, which one should be used as the primary autonomous vehicle technology, and which one as the secondary? As mentioned earlier, LiDAR is the industry’s first choice for driverless cars. Main reason being, cameras estimate the distance between objects based on relative size; however, LiDAR sensors know it with high precision.

However, Tesla’s CEO Elon Musk, who has long criticized LiDAR, recently called it “a fool’s errand” at Tesla’s Autonomy Day and claimed that everyone will drop it in the near future. As absurd as it sounds, Musk backed his claim with some reasoning (although it may sound unreasonable to most). First, LiDAR is costly hardware. Secondly, he demonstrated how camera is getting better and better at achieving sensor-level accuracy.

future of autonomous vehicle technology vision vs sensors

Currently, cost is undeniably the biggest let-down with LiDAR; however, all active players are putting an enormous amount of efforts to bring its cost down. For instance, in 2017, Google-backed Waymo claimed that it will build its own LiDAR sensors and will reduce the cost from $75,000 to $7,500 apiece. In March 2019, Waymo updated on its claim that it will soon start selling its in-house LiDAR devices to third-parties.

Another drawback, LiDAR becomes inaccurate in fog, rain, and snow, as it’s high-frequency signals detects these small particles and include them in the rendering. On the other hand, radar works quite effectively in such weather conditions and it also costs considerably less; therefore, is arguably a better substitute than LiDAR when used as secondary autonomous vehicle technology in conjunction with cameras.

Also Read: The Current State of AI in the Corporate World

What Future Holds for Autonomous Vehicle Technology

Given the ongoing developments, it is likely that the future will unfold either of the following two possibilities:

  1. Camera-based vision systems achieve LiDAR level accuracy
  2. LiDAR becomes cheaper than the cost of improving camera systems

Which will happen first is hard to say with any certainty. LiDAR prices have dropped over the last few years, so has the camera’s image processing. Tesla, with its Fleet Learning capabilities, is betting high on cameras and argues that its systems are designed to replicate human behavior and recognize the world around with colors and signs, not by distance. However, discarding LiDAR completely is also not an option at the current stage. So, the next few years will be crucial in deciding where the course of autonomous vehicles turns.

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Data Analytics

Moneyball was an eye-opener.

Today, it is a widely popular theory. But unfortunately, widely misunderstood too.

Most people watch the movie or read the book and say, “Wow, we should use more of this analytics to solve our business problems”. And that’s where they are wrong.

Moneyball theory isn’t about the importance of using analytics. It is about how you use it.

Analytics was being used in baseball for decades. What Billy Beane’s Oakland Athletics did differently in the 2002 season was that they went against the collective wisdom of baseball scouts and analytics, made decisions based on previously overlooked metrics and went on to become one of the most successful teams of the season despite being one of the most underpaid ones.

Let’s dig deeper into it to figure out what businesses can learn from the Moneyball theory and how they can use analytics in a way that would allow them to experience growth beyond what conventional business wisdom would.

Understand the Problem (Really understand it)

If you have seen the movie, there’s a very interesting and very tense round table discussion between Billy Beane and a bunch of experienced baseball scouts about ‘what the problem is?’ Here is the clip:

The point here is – in general, our industry experience helps us make better business decisions. But it also influences how we see and interpret a given problem. And sometimes it influences us to the point where it prevents us from seeing the problem from a different angle even when the previously tried and tested methods don’t seem to work.

Evaluating a given problem from different angles allows us to see different possible solutions – some of which can be better than the original one we are inclined to stick. In terms of analytics, it allows us to consider previously ignored metrics, which might be the key to the solution for that particular problem.

Trust Your Data (More than your guts)

The challenge with new, unorthodox solutions (like the one in Moneyball) is that quite often it would appear absurd or too risky. So, it is likely that you might find yourself in a situation where you experience, peers, and even your guts would tell you to go otherwise. But if you can see the clear logic in what your data suggests, then just stick with it. It will pay off.

When you are changing something, especially a well-established legacy process or system, you’re going to face the resistance. Not everyone will understand its potential, let alone accept it. The important thing is to be right and stick with it until you deliver results. Because sometimes the best way to convince people is to show them not to tell them.

Involve Leadership (Because data guys don’t make decisions)

In his Forbes article on Moneyball, Florian Zettelmeyer of Kellogg School of Management wrote: “Moneyball succeeded for the Oakland A’s not because of data analytics but because of Beane, the leader who understood the analytics’ potential and changed the organization so it could deliver on that potential.”

So, it is imperative that leaders sit down with the data teams, brainstorm with them, and use their industry expertise to assist them in coming up with the best possible strategies. Leadership’s involvement will also mitigate the resistance towards change and will accelerate the process of making analytics a part of the organization’s DNA.

To wrap things up, here are two images showing Oakland A’s standings in the 2002 (Moneyball) season:

How they stood salary-wise: 

data analytics moneyball oakland a's salary

How they stood performance-wise:

data analytics moneyball MLB standings 2002

Have you seen the Moneyball movie or read the book? Do you think we missed something? Let us know in the comment section below. We will add it.

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