As we are slowly entering the summer season, let’s look back and recap the latest automotive news of May. This month SAE updated the official names for ‘Autonomous Driving’ Levels, Germany passed legislation for autonomous vehicles driving without safety drivers’ presence, and Ford released its first electrical truck – the F-150 Lightning.
Apart from that, we kindly invite you to join our live panel discussion on ADAS testing that will take place in just a week. You can register for both German and English sessions – pick the one that fits you best.
There’s a lot of debate around what “autonomous driving” really is; and some pretty diametral view points – sometimes within one and the same company (looking at you, Elon Musk and Tesla’s legal department). One framework that’s proven to be useful in differentiating between what does and does not constitute self-driving technology are the SAE Levels Of Driving Automation (L0 – L5).
This standard, formally known as SAE J3016 has now been updated to more accurately separate driver support features (L0 – L2) and automated driving features (L3 – L5). It also clearly classifies simultaneous use of modern ADAS features like ACC and LKA as a Level 2 system – and thus firmly places it in the driver support domain. So get the latest “cheat sheet” and you’ll be well prepared for the next heated ADAS vs. AD debate – which we probably all get into at some point.
Speaking of automated driving: In a move sure to surprise many, Germany’s national parliament has voted to allow testing of Level 4 systems on public roads from 2022 – without a safety driver on board. Some restrictions such as proper insurance and remote shutdown options apply, but those hardly seem like roadblocks for companies serious about this type of technology.
With several OEMs in the country as well as players like Argo AI and Mobileye already testing their cutting-edge systems on public roads in Germany, it will certainly be interesting to see what to actually expect on and off the Autobahn next year – and how the public will react.
When is a car not a car? When it is a truck – or perhaps even something else entirely. Ford has revealed the battery electric version of its best-selling truck, dubbed the F-150 Lightning – and while thorough, independent reviews will need to be considered, it seems that competitors such as Rivian or Tesla’s Cybertruck will be facing a formidable competition:
The product management at Ford seems to have employed impressive user centricity; completely rethinking what a truck actually is – or can be. The F-150 Lightning is not only powered by a battery: It will also power external appliances, from work crews’ tools all the way to complete households, if necessary in a blackout: Something that will arguably be a selling point for citizens of US states regularly threatened by flooding, tornadoes or wildfires. For companies that run work crews (perhaps the most important customer segment for this vehicle), the BEV version of the F-150 may well turn the question of “Why go electric” into “Why not”: The use of a vehicle that makes both the job and fleet management easier (thanks to improved telematics) and that can easily be recharged back at the company lot every night seems compelling – even more so if it also brings maintenance costs down, which are typically higher for combustion engine vehicles.
You may remember our article about ADAS testing from last month’s newsletter, exploring a solution to create and leverage reference data at scale that we co-created for Porsche. Now we bring the contents to you live, as a webinar together with our partners:
Join atlatec CEO Henning Lategahn as well as representatives from GeneSys, MdynamiX and the Kempten University of Applied Sciences on June 8th or 10th:
We are offering sessions both in English and German language. So far the resonance has been amazing; which is why we’ve upgraded our webinar hosting package to allow for additional registrations. So if you haven’t already, you are warmly invited to sign up – we hope to see you next week!
I hope this overview helps you to stay on top of industry news. Make sure to watch the latest fire-side chat with the atlatec team. The video is already available on YouTube.
Stay tuned for the atlatec industry newsletter coming in the end of June!
News from TuSimple, Motional, New Flyer, AIMotive, and MathWorks
As usual, at the end of the month the atlatec team prepares for you a short overview of the automotive news that we found the most interesting. Enjoy the summary and make sure to watch our latest Zoom talk – it is already available on YouTube.
There has been a lot of news from China about robotaxi rollouts in the last few months; now comes a huge leap for autonomous trucks: TuSimple, a 4 year old startup has received approval for operating a fleet of 5000 fully self-driving trucks, without safety drivers on board.
This is also interesting news for investors in the space: TuSimple expects to turn a net profit of $300 million thanks to this move – while eyeing an IPO in 2021 that might lead to a $7 billion valuation.
Motional, the joint venture by Hyundai and Aptiv, will begin to offer driverless rides in Las Vegas, joining companies such as Waymo and Cruise. A “safety steward” (with somewhat unspecified responsibilities) will apparently be on board, but the permit issued by the state of Nevada allows for an empty driver’s seat.
An interesting detail is that operations are reportedly focused on “suburban residential areas”, which arguably make for a good use of AVs: Offering a bridge across the “last mile” gap between public transit stations and people’s homes might make more sense that deploying an ever-rising number of vehicles in city centers, where public transportation is usually at its best and most dense.
Speaking of public transportation: Why are we reading so much about autonomous trucks and robotaxis, but rarely hear of autonomous buses? Reasons behind that might be the challenge of navigating massive vehicles in dense, busy urban environments – but apparently New Flyer, North America’s biggest producer of buses feels up to that: Their first autonomous model, an electric Xcelsior, will begin testing in 2022.
There’s also advantages over other AV use cases according to New Flyer president Chris Stoddart: “One of the nice things is the ability to pre-map the routes, when you can run your vehicle around that route and pre-map it so that you have some redundancy and don’t have to rely completely on your various visual systems all the time […] When your average bus speed is only 12.5 mph that certainly helps.”
There’s lots of providers of tools for AV/ADAS simulation, and it mostly seems they’re sticking to their own devices, attempting to build the best solution they can independently of other players in the space. It’s a refreshing change to see some collaboration here, with AImotive and MathWorks integrating their “aiSim” and “RoadRunner” offerings:
This will apparently allow for an easy import of road models created in RoadRunner (formerly by VectorZero) into aiSim, an ISO 26262/ASIL-D-certified simulation platform. Since RoadRunner in turn provides the ability to import real-world OpenDRIVE HD maps (e. g. by atlatec), this might indeed make for a compelling toolchain, coupling access to realistic environment models with sophisticated virtual sensor simulation. If you happen to be using/trialing this solution, we’d love to hear some impressions!
We hope you enjoyed this issue. Stay tuned for the upcoming automotive news overview at the end of March. Get the overview directly to your mailbox – sign up for the atlatec newsletter.
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 …
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.
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.
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.
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.
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 Carla, VTD 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!
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.
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:
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.
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.
Netflix is great. You browse the catalog and pick the movies you like. It takes minutes only. It’s quick, it’s easy and it’s cheap. Selecting a movie and start watching is a lot faster than producing one and, well, less expensive.
We offer real-world data for simulation just like netflix, too. We have a database of ready-to-use 3D maps that serve as content in virtual environments and are in use for ADAS and AV simulation around the globe. These 3D maps are augmented with real-world traffic and enable scenario-based virtual validation. You, too, can look around and pick what suits your specific needs, pay for the subscription and only as long as you need it. You start testing immediately.
The image below shows the real world and its digital clone side-by-side.
Using simulation to validate ADAS and AVs is commonplace today. The burden of testing every feature, every update and every setting in the real world is unbearable whereas validation and testing in virtual environments can be done overnight. Many excellent software suites have emerged over the last years and the list in the comments section below shows many of them.
Software alone, however, is not enough. Simulation becomes as random as the real world only when backed by real-world data such as 3D maps and scenarios. Waymo is a prime example of following this approach. Combining state-of-the-art simulation software with real-world content is key. A recent TrechCrunch article suggests to prioritize and invest in virtual testing.
Prioritize and invest in virtual testing. Developing and operating a robust system of virtual testing may present a high expense to AV companies, but it also presents the opportunity to dramatically shorten the pathway to commercial deployment through the ability to test more complex, higher risk and higher number scenarios.
Mountains of canned, real-world content reduce the price for an entry ticket into this ability to test more complex, higher risk and higher number scenarios.
We offer a database of 3D maps from North America, Europe and Japan and offer it as a subscription. These 3D maps capture the real world and make it available for use in your existing simulation ecosystem. In addition to the 3D maps we add an additional layer containing moving objects. Maps and moving objects form scenarios of short length (seconds to minutes) that stress-test your ADAS or AV stack. Scenarios can be modified with our software to form edge- and corner cases. The best part is: The database grows continuously providing more and more cases over time. And, you pay only for what you need.
The world-to-simulator transform is shown in the images below.
We launch our database subscription with pilot customers now and we need your help. Just like netflix, too, we need to understand what you would like to have (is it Lord of the Rings, Frozen or Sharknado?). If you would like to talk to one of our experts then please contact us via email or schedule a meeting with one of our mapping experts.
This blog article was written by atlatec founder and CEO Dr. Henning Lategahn. Feel free to reach out to Henning via LinkedIn.
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.
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:
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, 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, 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.
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!
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:
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.
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
We hope you enjoyed your Christmas break and had a good start into 2021. As we already approached the end of the first month of this year, let’s look back at the articles and companies that drew our attention.
Enjoy the monthly overview that the atlatec team has prepared for you:
When it comes to autonomous vehicles, everyone loves to debate safety and how to validate it. It is indeed a formidable challenge, and so far, most AV companies have gone their own ways in dealing with this, publishing disengagement reports and similar. This has been controversial, however, since there are no standards and the numbers therefore can’t really be compared – this may now change in the US:
The Department of Transportation has launched the VOICES program (Virtual Open Innovation Collaborative Environment for Safety) which aims to “allow for testing in a representatively complex and connected multi-system environment without having to leave the sanctity or privacy of your development lab.”
Since a lot of our data goes into the domain of virtual validation/verification, we know firsthand that this is a very diverse domain so far – it will be very interesting to see what materializes under the new US administration.
GM changes their logo – so what, one may ask? This is not a design newsletter, after all. Their reasons for doing so – and the fact that it’s the first time in over 50 years – are highly relevant, however:
The new GM logo is designed to reflect their all-in commitment to electric vehicles (with some elements supposed to liken electric connector plugs), and it comes with a marketing campaign titled “everybody in” to further underline that this indeed where all of GM intends to go. With GM’s recent announcement to spend up to $27 billion on electrification of their portfolio and now the new logo, it seems there’s no more room for debate whether electric vehicles will become mainstream fast.
It’s not just GM who are banking on cleaner mobility. With Covid-19 having had a major impact on a lot of automotive companies, investments in many areas have dried up – electrification is not one of them:
Rivian, a Bay Area startup that is aiming for electric trucks and delivery vans (the latter to be supplied to Amazon, for example), have kicked off the new year with a new investment round, bringing in $2.6 billion. This raises the total money raised by the company to about $8 billion and their valuation to over $25 billion, apparently – before having delivered a single vehicle.
Over the last 2 years, there’s been a clear trend regarding how OEMs view the use of HD maps in production vehicle ADAS technology: While there used to be a debate whether this is really necessary, it seems that by now virtually every car maker (except Tesla) agrees that HD map data will be a requirement to support L2+ systems and anything above.
One very interesting endeavor in that domain is “crowdsourcing” the data for creation or maintenance of those maps: Using sensor data collected from the entire production fleet while the vehicles are being driven in customers’ daily lives.
Mobileye is one company that is heavily invested in doing just that – and announced at CES that their test vehicles will come to Detroit, Paris, Shanghai, Tokyo and perhaps New York this year (following deployments in Jerusalem and Munich). As a mapping company, we’ll be following the reception closely – as well as which other sensor makes might follow suit.
I hope this short overview helps you to stay on top of automotive news.
Stay tuned for the upcoming industry newsletter at the end of February!
The video talk with atlatec team is already available on YouTube. Feel free to watch it and share your feedback with us.