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Cargo Protruding from Vehicle

skip to Main ContentHuman drivers confront and handle an incredible variety of situations and scenarios—terrain, roadway types, traffic conditions, weather conditions—for which autonomous vehicle technology needs to navigate both safely, and efficiently. These are edge cases, and they occur with surprising frequency. In order to achieve advanced levels of autonomy or breakthrough ADAS features, these edge cases must be addressed. In this series, we explore common, real-world scenarios that are difficult for today’s conventional perception solutions to handle reliably. We’ll then describe how AEye’s software definable iDAR™ (Intelligent Detection and Ranging) successfully perceives and responds to these challenges, improving overall safety.
Download AEye Edge Case: Cargo Protruding From Vehicle [pdf]
Challenge: Cargo Protruding from VehicleA vehicle equipped with an advanced driver assistance system (ADAS) is driving down a road at 20 mph. Directly ahead, a large pick-up truck stops abruptly. Its bed is filled with lumber, much of which is jutting out the back and into the lane. If the driver of an ADAS vehicle isn’t paying attention, this is a potentially fatal scenario. As the distance between the two vehicles quickly shrinks, the ADAS vehicle’s domain controller must make a series of critical assessments to identify the object and avoid a collision. However, this is dependant on its perception system’s ability to detect the lumber. Numerous factors can negatively impact whether or not a detection takes place, including adverse lighting, weather, and road conditions.
How Current Solutions Fall ShortToday’s advanced driver assistance systems (ADAS) will experience great difficulty recognizing this threat or reacting appropriately. Depending on their sensor configuration and perception training, many will fail to register the cargo before it’s too late.
Camera. In scenarios where depth perception is important, cameras run into challenges. By their nature, camera images are two dimensional. To an untrained camera, cargo sticking out of a truck bed will look like small, elongated rectangles floating above the roadway. In order to interpret this 2D image in 3D, the perception system must be trained—something that is difficult to do given the innumerable permutations of cargo shapes. The scenario becomes even more challenging depending on time of day. In the afternoon, sunlight reflecting off the truck bed or directly into the camera can create blind spots, obscuring the cargo. At night, there may not be enough dynamic range in the camera image for the perception system to successfully analyze the scene. If the vehicle’s headlights are in low beam mode, most of the light will pass underneath the lumber.
Radar. Radar detection is quite limited in scenarios where objects are small and stationary. Typically, radar perception systems disregard stationary objects because otherwise, there would be too many objects for the radar to track. In a scenario featuring narrow, non-reflective objects that are surrounded by reflections from the metal truck bed and parked cars, the radar would have great difficulty detecting the lumber at all.
Camera + Radar. Due to the above explained deficiencies, in most cases, a system that combines radar with a camera would be unable to detect the lumber or react quickly. The perception system would need to be trained on an almost infinite variety of small stationary objects associated with all manner of vehicles in all possible light conditions. For radar, many objects are simply less capable of reflecting radio waves. As a result, radar will likely miss or disregard small, non-reflective stationary objects. In addition, radar would be incapable of compensating for the camera’s lack of depth perception.
LiDAR. Conventional LiDAR doesn’t struggle with depth perception. And its performance isn’t significantly impacted by light conditions, nor by an object’s material and reflectivity. However, conventional LiDAR systems are limited because their scan patterns are fixed, as are their Field-of-View, sampling density, and laser shot schedule. In this scenario, as the LiDAR passively scans the environment, its laser points will only hit the small ends of the lumber a few times. Typically, LiDAR perception systems require a minimum of five detections to register an object. Today’s 4-, 16-, and 32-channel systems would likely not collect enough detections early enough to determine that the object was present and a threat.
Successfully Resolving the Challenge with iDARAccurately measuring distance is crucial to solving this challenge. A single LiDAR detection will cause iDAR to immediately flag the cargo as a potential threat. At that point, a quick series of LiDAR shots will be scheduled directly targeting the cargo and the area around it. Dynamically changing both LiDAR’s temporal and spatial sampling density, iDAR can comprehensively interrogate the cargo to gain critical information, such as its position in space and distance ahead. Only the most useful and actionable data is sent to the domain controller for planning the safest response.
Software ComponentsComputer Vision. iDAR combines 2D camera pixels with 3D LiDAR voxels to create Dynamic Vixels. This data type helps the system’s AI refine the LiDAR point cloud on and around the cargo, effectively eliminating all the irrelevant points and creating information from discrete data.
Cueing. As soon as iDAR registers a single detection of the cargo, the sensor flags the region where cargo appears and cues the camera for deeper real-time analysis about its color, shape, etc. If light conditions are favorable, the camera’s AI reviews the pixels to see if there are distinct differences in that region. If there are, it will send detailed data back to the LiDAR. This will cue the LiDAR to focus a Dynamic Region of Interest (ROI) on the cargo. If the camera lacks data, the LiDAR will cue itself to increase the point density on and around the detected object creating an ROI.
Feedback Loops. A feedback loop is triggered when an algorithm needs additional data from sensors. In this scenario, a feedback loop will be triggered between the camera and the LiDAR. The camera can cue the LiDAR, and the LiDAR can cue additional interrogation points, or a Dynamic Region of Interest, to determine the cargo’s location, size, and true velocity. Once enough data has been gathered, it will be sent to the domain controller so that it can decide whether to apply the brakes or swerve to avoid a collision.
The Value of AEye’s iDARLiDAR sensors embedded with AI for intelligent perception are very different than those that passively collect data. As soon as the perception system registers a single valid LiDAR detection of an object extending into the road, iDAR responds intelligently. The LiDAR instantly modifies its scan pattern, increasing laser shots to cover the cargo in a dense pattern of laser pulses. Camera data is used to refine this information. Once the cargo has been classified, and its position in space and distance ahead determined, the domain controller can understand that the cargo poses a threat. At that point, it plans the safest response.
Cargo Protruding from Vehicle —AEye Team Profile: Jim RobnettSAE's Autonomous Vehicle Engineering on New LiDAR Performance MetricsAEye Team Profile: Aravind RatnamAEye Team Profile: Amy IshiguroAEye’s New AE110 iDAR System Integrated into HELLA Vehicle at IAA in FrankfurtAbrupt Stop DetectionLeading Global Automotive Supplier Aisin Invests in AEye through Pegasus Tech VenturesAEye Team Profile: Vivek ThotlaRethinking the Three “Rs” of LiDAR: Rate, Resolution and Rangeprevious post: Previous ← False PositiveAboutManagement TeamAdvisory BoardInvestorsiDAR Agile LiDAR Dynamic Vixels AI & Software Definability iDAR in ActionProducts AE110 AE200 iDAR Select Partner ProgramNewsPress ReleasesAEye in the NewsEventsAwardsLibraryTechnologyNews & ViewsProfilesVideosBlogCareersSupportContact Back To Top

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Reviving The “Tesla Deathwatch” — With A Totally Different Spin

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Published on November 18th, 2019 |

by Frugal Moogal

Reviving The “Tesla Deathwatch” — With A Totally Different Spin

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November 18th, 2019 by Frugal Moogal

After the recent unveiling of the Ford Mustang Mach-E, I feel like it’s time to revive the Tesla Deathwatch.

No, not the Tesla Deathwatch that you may have read about on this site before, where certain websites made daily updates gleefully predicting that Tesla would soon run out of money and be forced to shutter operations (more than a decade ago).

Instead, as I mentioned in my previous article, the Mustang Mach-E may end up being a Ford killer. Simply put, Ford is carrying around $100 billion in debt. If the transition to electric mobility happens too quickly, Ford could be left with a ton of debt and factories full of internal combustion technology that end up being stranded assets. If this happens, then the attempt to transition from internal combustion technology to electric propulsion may end up leading to Ford’s demise.

Anyway, in this new sporadic series, I’m going to take a look at legacy automakers to see the risks that this transition has to them and how they are coping with it. I will also try to estimate the risk that Tesla has created through its disruption.

And that’s the thing — when new technology comes along, often the most vulnerable are those companies that dominated the previous technology, as they are too invested in that technology to make a sudden change. A sudden change renders a significant portion of their business obsolete immediately, while the new technology has no guarantee of profits.

There are lots of recent examples of this that I could use to highlight the change. It was anticipated that at any time Sears could destroy Amazon, but it’s pretty clear who is winning that battle. Blockbuster Video dominated the home movie industry, and then Netflix came along. Atari dominated the video game industry, so it stopped innovating and let Nintendo — who had even asked them to release the NES in the United States under the Atari brand — to take over.

Instead, I’m going to take a quick look at a company that I think is perhaps the best parallel to what is happening with automobiles right now, Kodak.

The Kodak Story
The Kodak story is an absolutely fascinating one, and I strongly suggest if this interests you at all taking some time to really research it, but here’s the basic overview:

In 1975, a Kodak engineer by the name of Steve Sasson invented the first digital camera. He was told not to tell anyone about it, with a former Kodak vice president directly admitting that they could not sell it because they feared the effect it would have on the film market, which was Kodak’s main source of revenue.

And film sales were great, peaking in 2001 as consumers had started to purchase digital cameras. Kodak faced a problem — the manufacturing process for its film, which was truly extremely complex to make, had nothing in common with making semiconductors to make great digital cameras. Add to that the fact that companies could buy the components to make a digital camera from various providers and create their own cameras quickly, without being encumbered with significant debt and assets from businesses that were suddenly losing market share.

And market share dropped quickly. The market peak of 2001 was followed by a few years of the market slowly contracting, following by that trend gaining speed like a snowball rolling downhill. By 2010, worldwide demand for photographic film had fallen to less than 10% of what it was in 2001. In 2012, Kodak filed for bankruptcy.

Perhaps even more interesting, even before the market peaked, Kodak realized that it would need to start selling digital cameras. In 1999, Kodak held a 27% market share on digital cameras. As a matter of fact, my first digital camera was a Kodak camera.

The problem was Kodak wasn’t making money on those cameras. Indeed, this incredible article from Reuters, written shortly before Kodak’s bankruptcy, noted that in 2001 Kodak was losing $60 for every digital camera it sold. It didn’t matter that Kodak was actually doing pretty well in digital camera market share — capturing over 20% of it in 2005. The combination of stranded assets in its core business and losing money to maintain market share was unsustainable.

Kodak emerged from bankruptcy in 2013, but is a shell of what it used to be. In Q1 2001, Kodak reported revenues of $2.975 billion and net earnings of $150 million. In Q1 2019, Kodak reported revenues of $291 million and losses of $16 million.

The Kodak Lesson
It’s difficult to turn a profit when your core, established business is in free fall and the only way for you to gain market share in the future of your market is to lose money on every sale you make. After the peak of film sales in 2001, and a few years of relatively small declines, the market for film nosedived at 20 to 30 percent per year.

Kodak was still making money on its film divisions when it went out of business — it is just that the business had shrunk to a point nearly unimaginable a few years before. In reading through Kodak’s annual reports, I found that Kodak made a profit of $1.43 billion on revenue of $10.231 billion for film production in 2000. By 2011, the company made a profit of only $34 million on revenue of $1.547 billion. I feel it’s also worth noting that Kodak lost $349 million on its digital camera division that year.

There are two lessons here that are important. The first is that markets for well established goods can collapse at nearly unimaginable rates as new technology disrupts the old. In 2000, absolutely no one expected that physical film sales would topple as fast at they did. In 10 years, Kodak lost 85% of its sales to the disruptive change that digital cameras brought about.

The second thing is that as markets shrink with well-established groups, so do the profits. In 2000, Kodak earned nearly a 14% profit on it film sales. By 2011, the company was earning 2%.

To me, the lesson is clear — technology transitions happen much faster than people and companies usually expect, and once they catch on, the companies leading the prior iteration of the industry are the first to go out of business.

Tesla is the force that has created incredible disruption within the auto industry. Whether you are a fan or a short seller, Tesla has proven that electric cars can be safer, easier to maintain, cheaper to operate, better for the environment, and more fun to drive than their internal combustion counterparts. They can also use established technology for connectivity to improve the car through time.

I’m not using this article today to look at any particular automaker, but I think the parallels with some are extremely strong. We’re entering a new era, and it’s just as likely that the next generation of automakers will be dominated by newcomers like Tesla, Rivian, and BYD as it is to be generated by the prior generation’s top manufacturers. In fact, if history is any indicator, it’s much more likely that the next generation will not be dominated by the prior generation’s manufacturers at all.

After all, Ford, GM, and Chrysler didn’t start by manufacturing horse buggies.
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About the Author

Frugal Moogal A businessman first, the Frugal Moogal looks at EVs from the perspective of a business. Having worked in multiple industries and in roles that managed significant money, he believes that the way to convince people that the EV revolution is here is by looking at the vehicles like a business would.

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