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News from Daimler Trucks, Waymo, Volvo Trucks, Honda, and atlatec

Your monthly automotive briefing

November has been quite an eventful month not only for automotive industry, but also for atlatec. We are happy to announce that our HD maps are compatible with one more simulation tool: Cognata

Also, our team is ready to present the result of the collaboration with TrianGraphics – the sample data is already available for download on our website.

Enjoy your monthly overview of automotive industry news!

Daimler Trucks partners with Waymo to build self-driving semi trucks – Via TechCrunch

When I initially saw the headlines about this, I was intrigued by words like “partnership” and “collaboration” between Waymo and Daimler Trucks North America. Upon closer reading of the press pieces, it turns out this partnership amounts to: Daimler selling trucks to a customer (who happens to be Waymo). Apparently, the Freightliner team at Daimler will not be involved in the “Waymofication” of the vehicles and have no insight whatsoever.

Seems like a lot of buzz for “OEM sells vehicles”, but serves to highlight the conflict of legacy OEMs and Silicon Valley software companies: Will the Daimlers of the world become the new Tier1s in the world of autonomous driving? Let’s wait and see – after all, Daimler Trucks still has its own AV project going on with Torc Robotics …

Volvo Trucks to electrify entire lineup by 2021 – Via electrive

There’s been a lot of news items this year about OEMs electrifying their model range; most recent examples including GM and Volkswagen, whose chairman called EVs “the only reasonable option” for the future.

One piece that was not quite as popular was this one from Volvo Trucks – which piqued my interest because electrification in commercial vehicles (save for buses) hasn’t been that much of a hot topic in my opinion. That might change quite soon, with Volvo promising EV options for their entire range, starting next year in Europe.

Mapflix for Simulation – Via atlatec

Cut down on delivery times and budget demands for HD maps: The atlatec OpenDRIVE database gives you instant access to over 1000 km of real-world HD maps. Our founder and CEO Dr. Henning Lategahn calls atlatec database “Mapflix for Simulation”: it is as easy to access and is cost-efficient.

Honda Wins World-first Approval For Level 3 Autonomous Car – Via International Business Times

It’s actually happening: Starting in Q1 of next year, the public will be able to buy a new Honda, capable of L3 automation – the first SAE level to actually be considered “automated driving” rather than “driving support”. To start, the vehicles will only be taking over operation on highways, and only in limited situations, such as stop-and-go traffic. To me personally, that’s one of the most tedious driving situations, though, so automating it should be a great value add for people in areas prone to traffic jams.

UK to ban sales of new diesel and gasoline cars in 2030 – Via CNBC

Easily the most underreported piece of news to me this month: The UK has decided to ban the sale of new petrol/diesel driven vehicles from 2030 (hybrids from 2035). Sure, Norway is 5 years earlier – but the UK is a rather different animal, both in terms of population and economy. While I feel this is an exceptionally brave move and hope to see it turn into a success, I remain somewhat sceptical: The required infrastructure alone will be a massive feat – and ten years can be a much shorter time, especially if you are also dealing with Brexit and a worldwide pandemic right when you start.

Atlatec joined forces with TrianGraphics to Create 3D Visualization of San Francisco HD maps – Via atlatec

And some more news from atlatec: We’ve released an expanded version of our San Francisco HD map sample – one that includes 3D assets and textures, for use in CarlaVTD and other simulations, entirely free! Visit the article to read more about the data, which was created in a collaboration with Trian Graphics, see a video and grab a download link. And if you do: Be sure to tell us what you think!


Just like last month, we got on a Zoom talk with Henning Lategahn and Tom Dahlström to discuss some of these news – the video is now available on YouTube. We hope you enjoy this issue!

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News from Daimler, Bosch, HERE, Mitsubishi, Tesla

December 2019 in Automotive Innovation

I hope you had lovely holidays and would like to extend my best wishes for the new year! 2020 is going to be an exciting one for us at atlatec, and I hope the same is true for you. Speaking of exciting stuff, here’s some news from our industry that stuck out this month – enjoy the read:

“Bosch and Daimler Launch San Jose Robotaxi Pilot” – via Forbes

Just a few weeks after Daimler’s new chairman Ola Källenius announced the company would cut down its investment in robotaxis, the car maker has now launched a pilot service in San Jose, collaborating with Tier1 supplier Bosch. The service is only open to a select group of pilot users (who are company employees) and there’s going to be both a safety driver and a separate engineer on board with passengers. However: “Daimler and Bosch hope to begin offering service to the general public in San Jose as soon as possible” – let’s see when that will turn out to be.

“Mitsubishi, NTT to buy 30% stake in digital mapping company HERE” – via Reuters

It’s been 4 years since BMW, Daimler and Audi teamed up to buy HERE from Nokia, aiming to build proprietary mapping competence rather than relying on (and being dependent upon) US tech companies. Now they’re going to share with Mitsubishi and Japanese telco provider Nippon Telegraph and Telephone (NTT). The goal, according to HERE’s CEO Edzard Overbeek: “[F]urther diversifying our shareholder base beyond automotive, which is important given the appeal and necessity of location technology across geographies and industries.”

“Tesla Release Electric Car Patents To Public” – via IFLScience

60 years ago, when Volvo invented the 3-point seat belt, they decided to open the patent to other car makers for free: The potential for saving lives was more important than clinging to intellectual property. If you thought such decisions for the common good couldn’t happen in today’s economy (like I did), Elon Musk proved you wrong this December, opening Tesla’s EV patents to other companies. The move might also make sense from a business standpoint, however: If it helps to drive the electrification of traffic as a whole, it stands to reason that more customers will look to buy an EV – and thus consider a Tesla.

“We Need to Move Beyond the Car” – via Cruise Automation/Medium

This one’s less of a news item in the sense that it describes a new technological feat by GM’s self-driving car company Cruise – but I felt it’s an important piece, taking a step back to reflect on the automotive industry’s overall approach and asking the question whether we’re even solving for the right problems: “Despite making up less than 1% of all vehicle miles traveled, ride-sharing has added further congestion, more emissions, and potentially even decreased safety in our cities from over-tired and overworked drivers.”

“Real-world road and traffic data for simulation” – via atlatec/YouTube

In closing, I have some atlatec news to offer in the form of a video: We are now able to offer real-world traffic data (in addition to maps) for use in simulators, such as IPG’s CarMaker. We and our pilot OEM customer for this technology are confident that this kind of real-world content will be very helpful for digital validation of AV/ADAS systems that are supposed to react to traffic and other moving agents, such as adaptive cruise control, cross-traffic alerts, adaptive high-beam control and more – what do you think?

That’s it for this month – have a happy new year and see you at CES in Vegas!


If you have any remarks about the pieces linked above, please don’t hesitate to leave a comment or reach out! I’m always happy to have a conversation and remain available by email or on LinkedIn. Speak soon!

Reminder: We also offer this monthly Automotive Innovation overview as a newsletter – if that sounds interesting to you, you’re more than welcome to sign up here.

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Scenario-based simulation: Combining HD maps and real-world traffic data

If you work in the ADAS/Autonomous Vehicles field, you are probably familiar with HD maps – virtual recreations of real-world roads including their 3D profile, driving rules, inter-connectivity of lanes etc.

A lot of these HD maps go into the simulation domain, where car makers and suppliers leverage them to train new ADAS/AV systems or for verification/validation of features from those domains. The reason to use HD maps of real-world roads (rather than just generic, fictional routes created from scratch) is simple: In the end, you want your system to perform in the real world – so you want to optimize for real-world conditions as early as possible, starting in simulation. As we all know, the real world is nothing if not random, and you will encounter many situations you would rarely find in generic data sets.

So far, so good: These HD maps can be used to properly train lane-keep assistance or lane-departure warning systems, validate speed limit sign detection and many other systems. However, a map only contains the static features of an environment – what about ADAS/AV features that are supposed to react to other traffic participants? Emergency braking systems, cross-traffic alerts or adaptive cruise control are all required to perform differently, depending on how other cars, bikes or pedestrians around the vehicle are acting. For proper training or testing of such systems in simulation, HD maps alone are not sufficient.

Scenario data: HD maps plus traffic data

The solution to bridge this gap is rather obvious: You add traffic to “populate” your HD maps. 

atlatec-scenario-static-dynamic
Scenarios consist of a static layer (the map) and a dynamic layer (traffic).

Real-world traffic, of course, is probably even more complex than real-world maps. Attempting to generically reconstruct the simplest traffic situations, such as a number of drivers coming to a halt at an intersection will fall short most of the time: Real drivers are human beings with infinite complexity, each with their own driving styles, preferences, vastly varying experience – and sometimes we have a bad day.

Once again, you want to optimize for real-world traffic situations while still in the simulation stage – so why not bring the real world into the virtual domain once more, capturing vehicles, pedestrians and more? The result are real-world scenarios, a perfectly aligned combination of HD maps and traffic, both built using data captured from survey runs on real roadways. Here’s a side-by-side-comparison, taken from an atlatec scenario:

As you may have noticed, not all connecting arms along the route are part of the HD map used for this scenario, so some vehicles seem to appear or disappear off-road. This is a perfect example of recreating only the features that are of interest for any given case – or to manipulate the data in ways that allows for testing of more rare cases: For example, you would want your front-view/radar system to correctly identify a vehicle pulling onto a road, even if it was pulling out “from nowhere”. This leads us right to the next topic: How do you use real-word data to simulate more extreme situations, or even accidents?

Scenario fuzzing: Manipulating real-world data for edge case identification.

If you want to find out where the limitations of your system lie, you will have to simulate some scenarios that are beyond their performance – failures, for short. This is of course a challenge: Looking at an emergency brake assist (EBA) as an example, you can hardly collect real-world data for instances where it failed – it is impractical to keep driving and hoping for an accident to occur to record it. This is where “scenario fuzzing” comes into play.

Using a toolchain that is optimized for scenario-based simulation, you can select certain variables of a scenario and manipulate them slightly. For example, you could raise the speed of the survey vehicle by a few km/h, or decrease the distance at which another car cuts in front of you. Keep doing this in ever so slight increments, and you will eventually end up with a fuzzed scenario where the EBA will no longer be able to prevent a crash – finding what’s commonly referred to as an edge case, or system performance limit. Combine the fuzzing for both variables (speed of the ego vehicle and cut-in distance), and you will identify which speed allows for which minimum distance and vice versa – resulting in a corner case, an instance where two edge cases meet.

Here is a different example, showing a white vehicle pulling out to reveal a stationary car on the lane ahead – in reality, on the left, it pulls out with ample time left for an emergency braking maneuver. In the manipulated version, on the right, it pulls out later, leaving less time for the system to identify and react to the hazard:

atlatec hd map scenario fuzzing
Reproduced real-world scenario (left) and a slight variation, after scenario fuzzing (right).

Leveraging scenario fuzzing of recorded data allows you to reap both benefits: Enhancing simulation realism and relevance by using real-world data and identifying edge/corner cases by incrementally manipulating scenario variables.

To discover this topic in more depth, and to see some video examples (including the one where the above screenshot was taken from), we recommend the presentation “Edge Case Hunting in Scenario Based Virtual Validation of AVs” from this year’s “Apply & Innovate” hosted by IPG Automotive:

Recording scenarios during Field Operational Testing

One opportunity to encounter a multitude of relevant scenarios or even edge cases in the real world is the Field Operational Testing phase (FOT): This is when OEMs, Tier1s or their partners conduct test drives on open roads, over thousands and thousands of kilometers. These test drivers take place when a system is considered safe enough for testing in public, as a prerequisite to final approval by regulators.

Of course, it is not uncommon to spot ADAS performance issues in the FOT phase – that’s what it’s for, after all. Typically, these will be issues that occur very rarely, either in very specific situations (such as near-edge cases) or only after a certain time of operation – this is, after all, the first time a system is being tested at scale in reality.

When such a situation occurs, it is a treasure trove of information for validation and verification engineers: All the onboard data recorded during these drives is analyzed in as much detail as possible, attempting to identify the cause and to fix errors, if there were any. However, onboard data will only tell you what a system “thought” happened: If you are hunting for false negatives or false positives (e. g. in sensor data), you need to match this data against what really happened.

To this end, you can to leverage scenario recording during FOT: When test vehicles are equipped to record HD map and traffic data, you can recreate the exact situation in which a system failure or near-failure occurred – including the precise road layout, a vehicle’s precise position and pose as well as the traffic that occurred around it at that time.

atlatec scenarios hd maps
Sensor hardware for scenario production mounted on a vehicle.

Replaying these “micro scenarios” in simulation allows for much more comprehensive insights into the situation surrounding a performance issue identified during FOT. Additionally, by fuzzing the data you can play around with infinite “what if” questions, further drilling down into the precise cause and severity of any errors. 


If you have any questions or would like to discuss how to leverage scenarios for your work, don’t hesitate to reach out via email or request a meeting with us. 


Author: Tom Dahlström, atlatec Gmbh

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Introducing atlatec HD Maps for Cognata

There are a lot of differences of opinion in the autonomous vehicle space, but one thing everyone can agree on:

Virtual training and validation of AV/ADAS systems and components are a key factor in achieving the massive scale of testing which is necessary.

To this end, we at atlatec are always working to support more simulation tools, allowing our customers to continue working with their toolchain of choice when leveraging our HD maps.

Today, we are happy to announce the newest addition to our list of supported simulation software: Cognata.

Cognata is a cloud-based simulation platform designed specifically for autonomous driving and ADAS applications and is used for AV training, validation and analysis. It offers several datasets to test AV components against, such as traffic lights, signs, pedestrians and vehicles.

By importing atlatec HD maps, Cognata customers will have the added benefit of training and testing on road environments that are highly accurate digital twins of real-world routes, ensuring a more robust system performance and similar results to a real drive-test. The maps are supplied in the OPENdrive format.  

If you are a Cognata user and interested in learning more about how to leverage our HD maps, please reach out via email or schedule a call with us.

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News from Tesla, Waymo, Cruise, Lynk & Co., atlatec

Another month is over – and while Covid-19 seems to be flaring up again all over the world, it’s not the only news out there: The automotive industry, which was hit hard by the Corona crisis has produced some interesting news items this October. Here’s my personal overview of what stuck out:

European Safety Assessment Slams Tesla Autopilot for Its Inability to Keep Drivers’ Attention – via The Drive

This month, Tesla released the beta version of its “Full Self-Driving” system to a limited batch of paying customers. The resonance has been mixed and there’s lots of video and more out there, showing situations which FSD apparently handles well – or not. This article got a bit lost in the wake of all this – but I feel it emphasizes an underlying conflict of any self-driving tech relying on drivers’ attention: The better the self-driving performance and user experience, the less attention “drivers” will pay – and the less they’ll be prepared to take over in critical situations. Tesla’s user experience is apparently the worst at keeping drivers’ attention in auto mode, as per this recent NCAP analysis.

Bonus: I also recommend a look at this Twitter thread by Voyage CEO Oliver Cameron who took the time to analyze footage from one of the first test drives in detail.

Waymo will allow more people to ride in its fully driverless vehicles in Phoenix – via The Verge

Waymo, arguably a leader in the autonomous vehicles domain reached another milestone this month: The Google company will open up its driverless robotaxi service in Phoenix to about 1 000 app users, who can now request rides without safety drivers onboard. Remote operators will be on standby to take control of the vehicles if necessary, but Waymo expects little work for them.

Cruise can now test driverless vehicles on the streets of San Francisco – via The TechCrunch

Cruise, a subsidiary of General Motors, is not yet offering rides to the public but got approval by the Californian DMV “to test five autonomous vehicles without a driver behind the wheel on specified streets within San Francisco.” This is the fifth permit for driverless testing after Waymo, AutoX, Nuro and Zoox and it comes with some restrictions: “The Cruise vehicles are designed to operate on roads with posted speed limits not exceeding 30 miles per hour, during all times of the day and night, but will not test during heavy fog or heavy rain, the DMV said.” 

There’s an ongoing discussion about the ethics of these public-roads tests: On the one hand, companies are supposed to “verify vehicles are capable of operating without a driver” to get a permit, but on the other hand those tests are being conducted with the specific purpose to verify this in the first place – they are tests, after all. This has potential for further controversy, and further underlines the need for comprehensive, real-word-based simulation ahead of on-road operations.

Lynk & Co’s compact SUV costs €500 a month but might earn you a profit – via The Verge

This was relatively unnoticed news this month, but I find it worth noting because GEELY’s Lynk & Co. brand is attempting to redesign one of the basic fundamentals in automotive: The relation between car ownership and access to (car-based) mobility.

What Lynk & Co. is offering with the new “01” model is a built-in car-sharing platform, complete with mobile apps to unlock vehicles by phone etc. Individuals can take out a lease on a 01 (around 500 EUR a month, including service by Volvo dealerships) and then offer it for use via the platform – defining when it’s available and how much they want to charge to rent it out to other users, who don’t pay for vehicles/leases themselves. Sure, car sharing is nothing new – but if done right, this could bring a new level of convenience to the game which might really make a difference.

I find this move a) very brave, because it essentially means a commitment to sell less cars by GEELY and b) very innovative to come from an OEM because it doesn’t attempt to solve any and every mobility challenge by adding more, or better vehicles but instead truly treats mobility as a commodity. The new car – and service – will pilot in Amsterdam, arguably one of the major European cities which has done most to move away from traditional car ownership models.

How Accurate Are HD Maps for Autonomous Driving and ADAS Simulation? – via atlatec

It’s definitely one of the most frequently asked questions for us here at atlatec: “How accurate are HD maps”? It sounds innocent, but answering it correctly is rather complex. However, we feel that the question is important, both when it comes to safety in autonomous vehicle operations and regarding the validity of simulations based on real-world maps.

This month, we’ve therefore taken the time to answer the question comprehensively; taking a close look at what accuracy really means in the context of HD maps – and of course we’re also putting numbers to what atlatec achieves in this domain.


As a first this month, we took to Zoom to discuss some of these news items internally – and we recorded it: Tune in to hear what our CEO, Henning Lategahn thinks about the developments at Tesla and Lynk & Co. and for a some more explanation on the topic of HD map accuracy on our YouTube channel!

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How Accurate Are HD Maps for Autonomous Driving and ADAS Simulation?

In our mission to create digital twins of real world roads, our team at atlatec has taken on a number of HD Mapping projects all over the world, delivering HD maps and 3D models for autonomous vehicle operations and AV/ADAS simulation.

Along the way, we’ve discovered a number of topics and questions that are of relevance to almost all project partners involved – and we want to take the opportunity to discuss some of these in more detail. To start, we’ve decided to answer one of the most prominent and frequent questions we get:

“How accurate are HD maps?”

Maintaining high accuracy is one of the biggest challenges in building HD maps of real-world roads – and a rather complex one. Let’s dive right in and start by looking at what accuracy means in this context:

What does “accuracy” mean for HD maps?

With regard to accuracy, there are two main focus points that determine the quality of an HD map:

  • Global accuracy (positioning of a feature on the face of the Earth)
  • Local accuracy (positioning of a feature in relation to road elements around it).

It is important to note that, in terms of road mapping, accuracy is an index that cannot be derived from a single variable. With regard to our mapping technology, accuracy is directly dependent on 3 potential sources of error:

Survey-stage-atlatec
Stages of the mapping process and accuracy-related errors that may originate from them.

Global/GPS error

Global accuracy of maps is generally bound by the accuracy of GPS: This challenge is the same for map providers all over the world. With regard to this type of error, then, the main cause is poor GPS signal quality. It is most often affected when driving a survey vehicle in areas that are covered by roof-like structures (most commonly under bridges and through tunnels), as well as surrounded by tall buildings (within street canyons).

The challenge to accurately determine the position of ones sensor pod is a very old one. It’s basically the same that seafarers used to have when navigating by the stars: In order to accurately pinpoint your goal and chart a course, you need to first determine where you are located. Similarly, in HD mapping, you can’t answer the question “Where is this sign we’re detecting?” unless you first answer the question “Where are we currently positioned?”.

As a result, your ability to accurately survey a road and its surroundings is directly dependent on your ability to first pinpoint the position and pose of your survey vehicle – along all trajectories it was driven. Any errors in determining a sensor’s position and pose will subsequently result in a global accuracy error of the map created from this sensor’s data.

To maintain a high degree of global accuracy, our sensor pods contain survey-grade differential GPS sensors: This ensures optimized signal reception and allows to supplement the real-time satellite signal by using correction data from GPS base stations, which exist almost all over the world. In combination, such correction data significantly enhances the accuracy, compared to using only GPS satellite signals.

atlatec-gps-hdmaps
atlatec combines GPS satellite and base station data for optimal global accuracy.

Local error

Before a survey vehicle enters a tunnel and after it comes out at the other end, the differential GPS receiver usually provides accurate global coordinates to determine its position. However, as mentioned above, attempting to track its movement on a global scale whilst it is driving through a tunnel produces error – there is no GPS satellite signal underground.

This is where the importance of the stereo cameras comes in: Imagery that we collect from the two, calibrated cameras whilst driving through a tunnel allows us to compute and track the pose and motion of a survey vehicle by using computer vision technology. To further supplement accuracy, we add another, redundant sensor in the form of a motion sensor, or IMU (inertial measurement unit).

When it comes to the processing stage, then, we use sensor fusion to combine the data from the cameras, GPS and IMU to successfully reconstruct the trajectory of a survey vehicle and its surroundings, maintaining high accuracy throughout the entire data set. The advantages of using computer vision technology stand out in contrast to other systems that are mainly IMU-based: Their main side effect is that, in areas with no or poor GPS, the trajectory of a vehicle can go off (drift) and may only be corrected once GPS signal is recovered. In the context of autonomous driving, such errors can not be afforded.

Using imagery collected from the stereo cameras allows us to recreate a very consistent trajectory, even in GPS-denied areas. If the GPS signal is lost for a very long distance, though, drift/local referencing error will eventually occur, as is the case for all known approaches.

Camera-based-approach-atlatec

The benefit of using a camera-based approach – also called visual odometry – over an IMU-based system lies in the nature of how the error accumulates over time: whereas the total error of an IMU accumulates in a cubic fashion (at a factor of x³, with x being the distance travelled), atlatec’s vision-based approach only makes for linear accumulation of error.

mapping
Mapping in a tunnel: atlatec’s computer vision-based approach maintains high accuracy even in GPS-denied areas.

Sampling error

This type of error is caused by incorrect calculation of distance between a point of interest (for example a stop line or a traffic light) and a sensor pod camera. Local sampling, or annotation takes place after the collected data is translated into a 3D model and is the process of labelling features within this model, thus making them identifiable to simulation tools or autonomous vehicles. In other words, annotation is the process of translating 3D imagery, which humans can easily understand and process, into a vectorized “digital twin” which can be processed by algorithms and AI.

atlatec_hdmaps_intersection
A 3D model of an intersection, with lane geometry/topology and other features annotated.

In order to annotate road objects accurately, we use a combination of AI and manual work, which will be discussed in more detail later on.

What is atlatec’s approach to creating accurate HD maps?

Attempting to deal with all three causes of accuracy error in the practice of road mapping poses a number of challenges both in terms of software and hardware development. Our mapping technology employs a number of tools and solutions which allow us to achieve high HD map accuracy in a cost-effective manner.

Portable, camera-based mapping setup

At atlatec, we use a sensor setup that is mainly camera-based. Having two cameras and a GPS receiver at a fixed distance from each other in a small, portable box allows us to map roads worldwide with very little logistical difficulty: The metal case containing all sensors is the size of a suitcase and can be set up on any car in a matter of minutes.

Leveraging the survey-grade differential GPS and our computer vision expertise as explained above, we manage to accurately recreate all trajectories driven during data acquisition. As both our hardware and software are developed inhouse, the sensor pods’ configuration and the pipeline for processing the data from them are heavily optimized for each other.

atlatec technology

Loop closure

Strong emphasis on achieving extremely accurate loop closure is a crucial step in creation of coherent datasets. Our survey methods include driving on every lane of a road we set out to map, extending initial driving duration but ensuring higher data quality (and eliminating occlusion issues). The main reason why this increases mapping accuracy is that, by driving on every lane of the same stretch of a road, the same road object can be detected multiple times, enabling us to determine its global position more accurately. The process of bringing the sensor data from these multiple survey trajectories together into one consistent result is what’s called loop closure.

To exemplify this, let’s say a vehicle equipped with an atlatec sensor pod drives on a lane framed by dashed lane borders. The survey vehicle will drive on that lane at least once (the example of driving past a desired point on the road twice is represented in the schematic image below as trajectory a and trajectory b). Moreover, the vehicle will also drive on its neighboring lanes (if there are any) as part of the same survey session which starts and ends at the same location. In turn, once it comes to the annotation stage, we will be able to represent, for example, a corner of any individual dash as a point in a 3D map.

In complex cases such as sharp turns where a certain point can be absent in some trajectories, then, we will still be able to determine the position of a dash accurately. The reason for it is that, thanks to loop closure, our data sets are very coherent. That makes it possible to connect the data acquired from both stereo cameras and track key points from multiple trajectories in which they are visible.

atlatec-survey-vehicle
Capturing the same point multiple times allows us to accurately determine its position, even when it is hardly visible from certain perspectives.

Human-assisted AI

Our third and main strength is our software. Data retrieved from the stereo cameras, the GPS receiver and the IMU is first pre-processed in order to accurately reconstruct driving trajectories and mitigate potential incoherences from driving in areas with poor GPS signal. Following this stage, we use a combination of AI and manual work to reconstruct a broad spectrum of road objects in a virtual environment. Although our software can detect and identify a wide range of road elements accurately, integration of manual work is an important step in ensuring high accuracy and consistency throughout the entire map.

atlatec hd maps
Top view of an automatically processed map, showing lane geometry/topology annotated in color.

How accurate are atlatec HD maps?

Based on thousands of kilometers of HD maps we’ve created all over the world and the results of various tests and audits, we conclude that accuracy errors will be lower than the following for 95% of atlatec HD map coverage:

Global/GPS accuracy

In areas with good GPS reception we achieve a global accuracy of less than 3 cm deviation using satellite signals and correction data from base stations.

In GPS-denied areas, however, inaccuracy rises with distance traveled through the area, being largest in its middle. This means that the maximum GPS error can be expressed as a percentage of the distance traveled through a GPS-denied area: We have quantified this through repeated tests which indicate that this value is less than 0.5%.

For instance, if we drive through a tunnel that is 500 meters long, our GPS-based estimation of the global position of a survey vehicle will not deviate more than max. 1,25 m from the truth in the middle of that tunnel.

As this is still a relatively high margin of error, we leverage computer vision as discussed above to mitigate the error on a local level:

Local accuracy (drift)

By using computer vision technology to reconstruct the trajectory driven on any route we can work around GPS, keeping consistency and accuracy at a high level even in tunnels and urban canyons.

As mentioned above, the error that occurs when relying on visual odometry accumulates far slower than e. g. MEMS IMU-based approaches: Within a certain horizon (h) around a survey vehicle, the drift of the reference trajectory will contribute to an error of less than 0.1%*h.

For instance, a feature located at 20 meters distance from a survey vehicle will not be displaced by more than 2 cm due to local drift of the reference trajectory.

Sampling accuracy

Inside of a corridor of 10 meters width around the mapping trajectory, features in the finished 3D model can be surveyed with less than 4 cm deviation. At a larger lateral distance, precision will drop.

Which kind of accuracy matters most for HD maps?

We have taken on a number of mapping projects all over the world so far, a typical customer use case being the creation of 3D models for (ADAS) simulation. With that in mind, it is important to note that, when it comes to virtual testing environments, the relevance of the accuracy errors mentioned above can differ.

Usually for simulation use cases, a low local and sampling error are of highest significance. Meanwhile, global accuracy and GPS positioning are often irrelevant in this context. In fact, GPS receivers weren’t even a part of our sensor setup in the beginning: This is due to the nature of virtual testing, where what matters is that the local environment is reproduced accurately – e. g. in the process of simulating lane-keep assistance on a digital twin of real-world lane geometry. As long as the positioning of the vehicle in relation to road elements is correct, it usually does not matter where on the globe these road elements are located. We will discuss map development for simulation in more detail in a separate article.

If you want to see for yourself how atlatec data can boost simulation, you can download a free sample map of Downtown, San Francisco here – provided in the OpenDRIVE format, as supported by a growing number of simulation tools.

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News from atlatec, Tesla, Waymo, Nikola, Zoox

Your monthly automotive briefing

September is coming to and end and it is time for the monthly automotive briefing. Our team has been expanding recently and we are happy to welcome on board our new Marketing Manager Hanna Auseyenka. Hanna is taking over the monthly automotive digest for you. 

September has been quite an eventful month for the automotive industry. As some companies come one step closer towards self-driving future and gain approval to test their driverless vehicles on real roads, others, like Nikola Motors, face distressing news.

Here’s a short overview of articles that might be of interest for you.

Tesla’s ‘Full Self-Driving Capability’ Falls Short of Its Name – Via Consumer Reports

There has been plenty of controversy around what Tesla claims to be ‘Full Self Driving Capabilities’ and what specifically constitutes a ‘feature complete’ status for those. Consumer Reports has now undertaken an independent test of all these features – this article is a nice summary of their procedure and results.

Germany wants to permit driverless cars across the country by 2022 – Via TNW

Germany driverless car

Despite coming down hard on Tesla’s Autopilot and Full Self-Driving, Germany isn’t totally against the concept of self-driving cars. Several German politicians, including Chancellor Angela Merkel, met last week to discuss possible regulations of self-driving cars. 

Driverless Trucking Not Slowing Down But The Ride Is Getting Bumpier – Via Forbes

driverless trucks

During the recent Automated Vehicles Symposium, industry stakeholders from Waymo, Kodiak, TuSimple, Einride, Penske, PACCAR and others discussed what the future of self-driving trucks will look like. This article is a nice recap of the industry’s supposed turning point by AV veteran Richard Bishop.

Nikola founder Trevor Milton leaves amid fraud claims, shares plunge – Via New York Post

Nikola

Dramatic news for Nikola Motor, the company that aims to produce zero-emissions vehicles: Trevor Milton founder and executive chairman, is stepping down from the company’s board of directors.

Zoox gains approval to test autonomous vehicles without safety drivers in California – Via Venture Beat

zoox

September has been an exciting month for Zoox. It is now the fourth company to receive a driverless testing permit from the California Department of Motor Vehicles (DMV). The other three companies that already have their vehicles on the roads are Waymo, Nuro, and AutoX.

Partnership Between atlatec and Applus+ IDIADA Results in 330 Kilometers Scanned Catalan Roads and a Sample Set – via atlatec

atlatec_idiada_hd_map

Several months ago atlatec Gmbh partnered up with the Spanish company Applus+ IDIADA and scanned 330 kilometers of Catalan public roads to collect data required for the creation of high-definition maps (HD maps) to be used to validate autonomous vehicles. Scanned routes include highways, interurban roads, rural roads, and city streets. The routes were selected for their above-average probability of encountering challenging situations for connected and automated vehicles. 

The collaboration between atlatec and Applus+ IDIADA resulted in the release of a second set of free sample data. As of today, a sample set of Catalan 3D roads with inch-perfect accuracy is available for download on the atlatec website.

All the best

Hanna Auseyenka
atlatec Gmbh

P.S. Do you feel like we missed something? Feel free to send me over your worth-sharing industry news over LinkedIn.

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News from atlatec, AAA, Waymo, Uber and Lyft

Your monthly automotive briefing

August has passed – and while things seem to slow down a little during summertime, there were still some news items worth noting. Let’s get right to it, and allow me to start with the piece of news from us here at atlatec which I’ve been promising:

“German 3D Mapping Company Atlatec Is So Confident, Competitors Can Access Its Data” – via Forbes

We have finally released our new, entirely free HD map of Downtown, San Francisco! Ed Garsten interviewed our CEO Henning Lategahn and me on the contents and creation of the data – which we believe might well be the largest OpenDRIVE data set freely available – and our reasons for sharing it with the public. You can read his article on Forbes or jump right ahead and grab a download link for the map on our website: https://www.atlatec.de/getsampledata.html

“AAA research finds driving assistance systems do less to assist drivers, more to interfere” – via Augusta Free Press

AAA advocates for better testing of ADAS systems – and undertook a study to benchmark how current systems perform. The results are well worth noting: “[Over] the course of 4,000 miles of real-world driving, vehicles equipped with active driving assistance systems experienced some type of issue every 8 miles, on average. […] On public roadways, nearly three-quarters (73%) of errors involved instances of lane departure or erratic lane position.”

It seems the need for more (and more realistic) simulation and virtual validation will not go away any time soon …

“Waymo Taps Texas As Its Robot Truck Hub With Dallas Depot” – via Forbes

Some more details about Waymo’s branching-out into cargo transportation have been made public. Like TuSimple, Kodiak and other autonomous trucking companies, the Google spin-off has chosen Texas for their logistics testing, citing high freight volume and other favorable environmental factors as their reasons.

Waymo’s trucks will apparently be operating between Texas and New Mexico, in particular on the I-10, I-20 and I-45 interstates. With Tesla also building a new Gigafactory in Austin, it looks like the Lone Star State is becoming a new hot spot for automotive tech!

“Judge grants Uber and Lyft temporary stay in driver reclassification case” – via TechCrunch

While Uber and Lyft both aim for autonomous robotaxis to finally become profitable, that day still won’t come as soon as some may have thought. In the meantime, the mobility providers rely entirely on contractor drivers/gig workers to be operational. However, the state of California (and some Californian cities) have successfully argued in court that Uber and Lyft drivers are, in fact, employees: That would mean they have to be paid better and given benefits that contractors have no claim to. Uber and Lyft argue this would force them to shut down operations – and have now achieved a temporary stay of the reclassification.

There will be more development in the months to come, and I personally am very interested to see how this plays out.

That’s it for this month – I hope you all have an excellent summer, even with things being the way they are, pandemic-wise. Stay safe, however you spend it!

All the best

Tom Dahlström
atlatec

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News from Tesla, Polestar, Plus.ai, Waymo and FCA

Your monthly automotive briefing

Hello Dear Readers,

Summer has come, and for many people that brings a longing to travel somewhere on vacation. In Germany and Europe we’re now seeing signs of Covid-19 numbers once again going up, and our desire to reclaim mobility might well play a role in this.

However you’re spending your summer, I hope that you will remain safe and healthy. To support you in that, here’s some mobility that you can discover from a safe distance – welcome to your monthly overview of automotive industry news:

“German Court Bans Tesla ‘Autopilot’ Name For Misleading Customers” – via Forbes

Tesla has become the world’s most valuable carmaker – and it remains one of the most controversial, particularly regarding its cars’ self-driving capacities that are often lauded by Elon Musk. German courts are less enthusiastic: Rating the terms “Autopilot” and “Full Self Driving” as “misleadingly prohibited”, Munich’s Regional Court has banned all future use of them by Tesla in Germany.

Musk seems unimpressed: In other news this month, he’s announced that he is “very confident about full self-driving functionality being complete by the end of this year.” Probably not in Germany: The necessary functions would currently be illegal here, according to the court ruling.

“Polestar and Google could hold the key to what car infotainment should be” – via GearBrain

Automotive OEMs have traditionally refused to let go of their proprietary infotainment systems’ OS and UI. As a result, even in the most advanced cars you may frequently find yourself asking “Why can’t this be simpler, just like my smartphone?” In an industry-first move, Geely’s Polestar has chosen to ditch all that and instead embrace Andriod Automotive – meaning you can now use your Google Assistant not only to mirror and operate apps, but for vehicle features such as turning on the seat warmers and more.

While most reviewers agree that it’s not perfect (as anyone who owns a virtual home assistant will expect), the much-improved user experience seems to be a clear indicator of what can be made possible if customer experience is prioritized over customer control.

“In An Industry First, Plus.ai To Submit Their Self-Driving Trucks To Independent Testing” – via Forbes

This piece is by Richard Bishop, who is always worth reading if you’re following autonomous driving news. If you do, you will be familiar with the ever-popular debate around “disengagement reports” and other attempts to quantify the safety of AV systems: As they are put together and published by every company on its own, with no commonly-agreed metrics and non-transparent definitions, they often amount to little more than marketing material. Meanwhile, governmental and regulatory bodies struggle to put together a common framework for testing of AV technology.

Autonomous trucking startup Plus.ai has now taken an interesting step by itself: The Transportation Research Center (TRC) in Ohio will define and conduct fully independent testing of their technology. It will be interesting to see if this catches on and might put pressure on other companies to follow in similar footsteps.

“Waymo, Fiat Chrysler expand autonomous vehicle partnership” – via Reuters

It seems I can hardly write one of these pieces this year without touching on another collaboration among AV companies. This month, it’s Waymo’s turn again: Shortly after having communicated they’ll build L4 production vehicles with Volvo Cars, they’re now also partnering with FCA to build autonomous vans and similar light duty commercial vehicles. Fiat Chrysler in turn had just recently made public a new collaboration with Voyage, aiming for purpose-built robotaxis. It does indeed appear that AV startups that arise outside vehicle manufacturing companies frequently find themselves in a position where an OEM partnership is required for in-depth technology integration.

I know I promised some atlatec-related news for the July edition of this industry overview: It seems I have to apologize and ask you for a little more patience. We’ll be ready with something cool soon and I look forward to sharing it with you!

All the best

Tom Dahlström
atlatec

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News from Ford, NVIDIA, Mercedes-Benz, Waymo, Volvo, Amazon and Zoox

Your monthly automotive briefing

Another month gone, and once more it was full of big news in the automotive industry. Let’s dive right in; here’s my pick of items that stood out this June:

“Ford Mustang Mach-E hands-free driving to compete with Tesla, GM” – via CNET/RoadShow

Tesla has the Autopilot, GM is building out its Super Cruise to Ultra Cruise (see last months newsletter), and Ford? Well, apparently they’re not going to be outdone and just announced the “Active Drive Assist” which, according to the article “will enable hands-free motoring on more than 100,000 miles of divided highways in the US and Canada under specific circumstances.” The first model confirmed to come with this option is going to be the 2021 Mustang Mach-E – another example of autonomous driving and EV tech going hand in hand.

“NVIDIA And Mercedes-Benz Join Forces In An Autonomous Car Power Play” – via Forbes

If you still believe that NVIDIA is only in the AV space to drum up interest for its graphics cards, you might want to reconsider: This month it was revealed that Mercedes-Benz “will be employing NVIDIA’s new DRIVE platform technologies as standard equipment in all vehicles, starting in 2024.” With German automakers being notoriously hesitant to broadly adopt US tech in this area (Daimler was collaborating to build its own AV tech with BMW until one day before this announcement), this is a massive change of strategy in my view – and it will be interesting to see if other German carmakers might follow up with similar moves.

“Waymo, Volvo partner to develop electric robotaxis” – via TechCrunch

Speaking of new partnerships: Volvo, having just announced last month that they want to sell autonomous L4 production vehicles in 2022, are now collaborating with Waymo. This is apparently an exclusive deal, aiming to integrate the self-driving company’s AV technology with a new electric vehicle by Volvo, purpose-built for ride-hailing applications. This looks similar to the recently announced cooperation between FCA and Voyage, whose CEO Oliver Cameron has stated that he believes a vehicle partner is absolutely necessary for an AV company to reach the necessary level of systems integration. Personally, I’m all for inter-company collaboration, so I’ll keep my fingers crossed!

“Amazon Buys Self-Driving Company Zoox For $1.2B And May Rule The World” – via Forbes

Bit of a dramatic headline there, but still an interesting analysis by Brad Templeton, himself a former member of Google’s car team: Amazon has closed the deal on Zoox, a company looking to build robotaxis. It will definitely be interesting to see if Amazon treats this mainly as a lateral investment (Zoox’ leadership is apparently staying on) or if they will collaborate more closely: Autonomous delivery would obviously be a massive cost saver for Amazon if it could be pulled off. Either way, it’s probably going to be a while until we see results.

That’s it for this month, I hope you enjoy the read and the early summer. Oh, and I promise we will have some atlatec news of our own in the July edition – until then!

All the best

Tom Dahlström
atlatec