Author: Brian Holt

Brian leads the development of maps for Autonomous Valet Parking at Parkopedia. His background is computer vision and machine learning.

A Successful End to the Project!

November 24, 2020 /

We’ve done it!

Imagine if you could drive to a car park and then, instead of spending 20 mins driving around trying to find a place to park, you tell your car to park itself. The car goes off and finds a spot. When you’re ready you summon it to come and pick you up. A bit like valet parking, except your valet is always with you when you drive.

2.5 years ago Parkopedia, the University of Surrey and the Connected Places Catapult set out to answer a question; how will autonomous vehicles park?  We knew that this would be a difficult problem.  Parking in a multi-storey car park means that there is no line of sight to satellites, which means no GPS.  That said, GNSS (even RTK-GNSS) is not accurate in urban environments due to the canyon effect.  As a result, a vehicle would need to estimate where it is using on-board sensors such as cameras, inertial measurement units and wheel odometry.  We know that it’s possible to localise using LiDAR if a point cloud of the environment is available but LiDAR systems were very expensive. We chose therefore to focus on visual-inertial localisation.  

Our goal was to work out what maps would be required to support the autonomous vehicle. We set out to develop the localisation and navigation algorithms that best utilise these maps and to prove them on our own autonomous vehicle.  We had to ensure that any autonomous driving was done safely. Finally we wanted to find out what you, the general public, think of the idea of your car parking itself.

Project scope

The project scope was defined by these 5 objectives:

  1. Developing automotive-grade indoor parking maps required for autonomous vehicles to localise and navigate within a multi-storey car park.
  2. Developing the associated localisation algorithms – targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units – that make best use of these maps.
  3. Demonstrating this self-parking technology in a variety of car parks.
  4. Developing the safety case and prepare for in-car-park trials.
  5. Engaging with stakeholders to evaluate perceptions around AVP technology

After the 7th quarterly meeting we published an update on the current status that showed that most of the work was complete. All that was left was to deploy the localisation and navigation software onto our autonomous StreetDrone test vehicle. 

Demonstration

In the video above the software plans a route from the drop-off zone to the selected target parking spot when the driver presses ‘PARK’.  Next, the vehicle localises itself by estimating its position with respect to the Artificial Landmarks. A recursive Bayesian Filter fuses the observations with odometry information . Finally, the software plans a path back to the pick-up zone when summoned.  

With that demonstration we have completed all the deliverables for the AVP project and have successfully brought the project to completion on time and on budget!  Our thanks go to 

Throughout this project we learned that drivers value the convenience promised by cars that will park themselves.  This project has delivered a major breakthrough by identifying and overcoming obstacles to full deployment of AVP through the development of a technology demonstrator.  At some point in the next few years your car will be able to park itself and we’ll be proud to have played a role in making that happen!

Autoware AVP demo was Successful!

October 15, 2020 /

Two years ago Parkopedia joined the Autoware Foundation as a founding member. With that short space of time all the necessary software for a full-scale AVP demo has been developed and tested. Automated Valet Parking (AVP) is the ability of a vehicle to park itself and to return to the driver when summoned.

The Autoware AVP was a complete success! Parkopedia is proud to have supplied the map of the AutonomouStuff car park in San Jose, CA where the demonstration took place. Parkopedia also supplied the path planning software that plans the route the vehicle will travel. In this case from the drop-off zone to the desired parking spot and back to the pick-up zone.

This is further confirmation that Parkopedia’s high definition digital maps are indeed suitable for navigation of automated vehicles inside car parks.

AutonomouStuff car park map in OpenStreetMap format

As Dejan Pangercic writes; “The demonstration itself was the culmination of a week of intense effort from contributors around the world, working around the clock to put the finishing touches on the low-speed maneuvers needed to accomplish the park and retrieve functions. The flawless performance highlights the engineering quality built into the ground-up design of the open-source Autoware.Auto software stack, intended to serve as a strong starting point for companies developing autonomous systems for commercial, mission-critical, or safety-critical uses.”

Congratulations to the entire Autoware team for all the hard work that has paid off handsomely!

Demonstrating Automated Parking with Park and Summon functions

April 2, 2020 /

The Autonomous Valet Parking (AVP) project is a 30 month project funded by InnovateUK and the Centre for Connected and Autonomous Vehicles. It is scheduled to finish on 31 October 2020.

With more than three-quarters of the project timeline having passed, we are proud to say that we feel on track to achieve the project objectives within the allocated project time allowance, even with the current additional challenges of COVID-19 self-isolation restrictions.

Over the 5 objectives, the current status is as follows:

  1. Develop automotive-grade indoor parking maps required for autonomous vehicles to localise and navigate within a multi-storey car park. Parkopedia believes this map-based approach is the best way to achieve global scale for the roll-out of an Automated Valet Parking feature, which is likely to be the first SAE level 4 feature. This goal is 100% achieved, as Parkopedia has collected data in a number of car parks around Europe and is creating an inventory of maps to be able to supply to customers. Some of these maps have been made available to the research community under a Creative Commons license.
  2. Develop the associated localisation algorithms, targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units, that make best use of these maps. We have agreed to use Artificial Landmarks in the final demo for the project, and toward that effort this goal is 95% complete. Details about Artificial Landmarks and how they can be used for localisation are available in this blog post. For the remainder of the project the research effort will be directed towards localising with natural landmarks which is a much more difficult problem.
  3. Demonstrate this self-parking technology in a variety of car parks. This is well underway, and the outstanding work items now exclusively relate to integration with the map and localisation algorithm. Great care is taken to account for car park ramps, as by necessity, the low concrete walls are at their closest to the car at this moment. These ramps are considered to be the point of greatest risk as the localisation methods have to work extra hard when moving between floor levels. Also, the vehicle control algorithms need to account for gravitational acceleration of the car down the slope, and slowing it on the up-slope. After a lot of testing in simulation and more than 250 hours of in-car testing, we are pleased to have overcome this challenge, which you can see in action in the video below.
  4. Develop the safety case and prepare for in-car-park trials. Safety documents to cover the testing thus far have been published. A final document to cover demonstrations with large numbers of people is the last item outstanding. We have secured initial agreement for a final demonstration in a different car park to showcase the functionality.
  5. Engage with stakeholders to evaluate perceptions around AVP technology. We have engaged with the wider public around this technology and the results have been published.

The project recently held the 7th (of 10) quarterly review meetings where demonstrated the vehicle’s capabilities to the project steering committee and stakeholders.

The outstanding work items now exclusively relate to integration with the map and localisation algorithm, but we are confident of completing the project on time with our objective achieved. We are looking forward to the day this feature is available in a production vehicle!

Halfway and a successful public demo!

September 9, 2019 / / 0 Comments

The Autonomous Valet Parking project is a 30-month project funded by InnovateUK and the Centre for Connected and Autonomous Vehicles, due to end on 31 October 2020. We are five quarters in and a lot has been achieved:

  • We have created our first maps of car parks which we are trialling with customers,
  • The vision-based localisation algorithms are well advanced,
  • The stakeholder engagement work is complete,
  • The autonomous software to power our StreetDrone is ready to take out for a demo,
  • The safety case has ensured that we are in a position to demo this safely. 

At the CENEX-CAM event that took place at Millbrook Proving Ground UK 4-5 September 2019, we showed the progress made in the project at this halfway point. The main objective was to demonstrate that the Autoware-based software on the StreetDrone is able to control the vehicle by following waypoints consistently and accurately. The demonstration scenario consisted of three parts and reflects how we believe AVP will be used in real life. In the demo, you can see:

  1. The vehicle following a pre-defined set of waypoints to the designated parking spot, having been dropped off at a designated drop-off zone by its driver,
  2. The vehicle exiting the parking spot and driving to the pick-up zone (where the vehicle’s regular driver would collect it),
  3. A test of our automatic emergency braking, using the front-centre ultrasonic sensor on the vehicle.

This public demo was an important milestone for us to demonstrate our ability to control the vehicle using a PID controller for longitudinal (throttle) control and pure-pursuit for lateral (steering) control.

Localisation is done using the LiDAR with NDT matching. At this stage of the project we’ve limited the speed to 1m/s, this will double to 2m/s (5mph) in the future.

We are using the SAE convention for marking lamps, with green for manual control, blue when under autonomous control and red when an error state occurs. Adding the RGB LED lighting was done alongside the development work to enable switching between forwards and reverse in software while still in autonomous mode.

The safety case for the project combines operational and system safety. On the operational side we have a safety driver who can take over when a dangerous situation presents, and we also have system safety using the LiDAR and ultrasonic sensors, which will bring the vehicle to a stop to avoid driving into a hazard. We demonstrated Automated Emergency Braking using the ultrasonic sensor, following the testing and preparation done previously at Turweston.

Overall, the demo (done five times in two days) was well received, and we saw good levels of interest from delegates at the event, with lots of questions being asked about the project. It was a pleasure to speak to media and to delegates.

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We’ve learned a lot from our friends at Ordnance Survey and we look forward to hosting them and others at the upcoming Autoware Hackathon. Many thanks to them of all the help and for storing our StreetDrone overnight under their gazebo!

Over the remaining 13 months of the project, we will be working on navigation and localisation using maps, with a final demonstration of the end solution due to take place in Autumn 2020.

Why Autonomous Valet Parking?

February 12, 2019 / / 0 Comments

Why Autonomous Valet Parking, not robo-taxis, will lead the adoption in self-driving technology

Looking back on 2018, the press have reported it to be the year when the hype around self-driving “came crashing down” with the first driverless fatality in March 2018. The first driverless taxi service was rolled out but it didn’t quite have the impact that the industry was expecting.

Research on self-driving cars has been continuing for more than 30 years, starting with the pioneering work by Ernst Dickmanns on the PROMETHEUS project. A lot of work has taken place since then and is still ongoing, but the question remains: why has the problem of self-driving still not been solved in 30 years?

In a sense, the problem has been solved and autonomous driving is already here.  Heathrow pods, Greenwich pods, Easymile, May Mobility are live now, and many others are already in production.  What sets these simpler systems apart from the systems being developed by Cruise, Argo, Aptiv, Waymo, Uber, Aurora, Lyft, FiveAI and others, is that the environments into which they are deployed are constrained in some way.  

The biggest challenge faced by the developers of general purpose self-driving technology is the requirement to handle complex environments with unpredictable interactions. Waymo’s director of engineering recently summarised the challenge by saying that the final 10% of technology development is requiring 10x the effort required for the first 90%.  Where the environment is simpler or constrained, then self-driving technology reduces to that of autonomous mobile robots like the kiva.

A more realistic approach to deploying self-driving is therefore needed. The two major places where self-driving car technology are likely to be deployed are on motorways – constrained environments with very strict rules, limitations on cyclists and pedestrians – and low speed restricted environments like retirement villages and car parks.

Autonomous Valet Parking (AVP)

Parking is one of the most important challenges for a traveller, with a parking pain point experienced on 12% of UK journeys (19% in London); the average driver spends 6.45 mins looking for a parking space during each journey. With nearly 1 in 5 journeys already experiencing problem finding a space, AVP represents a way of solving not only the current parking pain point, but also improving the overall parking experience for the other 81% of drivers. BCG’s 2015 report showed that 67% of drivers are interested in “capabilities such as automated searching for parking spots and autonomous valet parking”. Bosch’s 2017 study found that two thirds of consumers want an automated parking feature.

A study by Allensbach in 2016 asking the question “When would you want a driver assistance feature to take over for you?” overwhelmingly showed parking as the most desirable feature.

When German consumers were asked by Bitkom in 2016 when they would be willing to hand over control to the vehicle, the answer similarly was for parking.

The idea of Autonomous Valet Parking is to mimic the Valet function available in selected car parks.  After driving to a suitable drop-off location at or near the entrance of the car park, and similar to handing the keys to a valet, the driver presses the “PARK” button on a specially designed app.  The car then drives off under autonomous control and finds a suitable place to park. When the driver wants the vehicle back, they will press “SUMMON” and the vehicle will navigate to the pick-up zone.

The Society of Automotive Engineers (SAE) classifies this as a Level 4 feature, in that it provides total automation under specific circumstances.

Based on publicly available information, almost all premium OEMs (Daimler, Audi, JLR, VW, BMW) are working on AVP pilot projects . The reason this feature is so desirable is that it:

  1. Improves the parking experience by allowing drivers to be dropped off at a convenient location (e.g. at the entrance of the car park closest to the desired location such as shops or food court), avoiding the inconvenience and stress of having to find a parking space.
  2. Utilises parking spaces more efficiently by tighter/double parking of autonomous cars, and optimally distributing these vehicles within the available parking real estate.
  3. Avoids unnecessary congestion and pollution through real-time dissemination of parking space availability to the connected autonomous cars.

In addition to the economic benefits, there are clear social and environmental benefits. Driving around looking for parking causes stress and frustration, costs, wasted time, missed appointments, accidents and congestion, noise pollution and CO2 emissions. IBM’s parking survey found that in addition to the typical traffic congestion caused by daily commutes and gridlock from construction and accidents, it is estimated that over 30 percent of traffic in a city is caused by drivers searching for a parking space. By reducing the need to circle looking for a space, AVP has the potential to significantly reduce unnecessary congestion and pollution.

With space at a premium in busy city centres, vehicles equipped with AVP technology could make use of the less desirable spaces that are further from the entrance, freeing up parking spaces closer to a desired destination for those without the technology.

In addition to the economic, social and environmental benefits to AVP, there are also some technical reasons why it is a good candidate feature for large scale public rollout.

1) low speeds mean much lower risk of damage to people, cars and infrastructure.

2) a constrained environment means that the complexity of interactions with other actors has the potential to be significantly reduced.  

3) the cost of the required sensor suite and hardware platforms is lower because of the reduced risk and lower speeds.

Conclusion

This consortium’s key objective is to identify obstacles to the full deployment of AVP through the development of a technology demonstrator. It aims to achieve this goal by

  1. Developing automotive-grade indoor parking maps, required for autonomous vehicles to localise and navigate within a multi-storey car park.
  2. Developing the associated localisation algorithms – targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units – that make best use of these maps.
  3. Demonstrating this self-parking technology in a variety of car parks.
  4. Developing the safety case and preparing for in-car-park trials.
  5. Engaging with stakeholders to evaluate perceptions around AVP technology.

Autonomous Valet Parking is a low cost, low risk and high reward feature that consumers want.  It makes sense then to expect that this feature will be the first fully autonomous feature (at level 4 or 5) available to the general public.  Through Parkopedia’s autonomous valet parking project, we are actively working to make that desire a reality.

Autoware

January 30, 2019 / / 0 Comments

Parkopedia’s mission is to improve the world by delivering innovative parking solutions. Our expertise lies within the parking and automotive industries, where we have developed a solid reputation as the leading global provider of high quality off-street and on-street parking services.

Parkopedia helped found the AVP consortium because we believe that Autonomous Valet Parking will become an important way in which we can serve our customers, by reducing the hassle of the parking experience. Parkopedia are providing highly detailed mapping data for off-street car parks, one of the critical components to a car being able to successfully park autonomously.

To make Autonomous Valet Parking a reality, the consortium first selected the StreetDrone.ONE as its car development platform. We are now developing the software stack to run on our StreetDrone with NVIDIA Drive PX2. The University of Surrey, another founding member of the AVP consortium, provides the camera-based localisation algorithms needed for the car to navigate autonomously inside a parking garage, which will support vision, in addition to LiDAR-based localisation.

Parkopedia has joined the Autoware Foundation as a premium founding member, along with StreetDrone, Linaro/96Boards, LG, ARM, Huawei and others. We believe in open source as a force multiplier to build amazing software, and the AVP consortium is committed to using Autoware as the self-driving stack which will run on our StreetDrone and PX2 to demonstrate Autonomous Valet Parking.

Autoware was started in 2015 by Professor Shinpei Kato at the Nagoya University, who presented it at ROSCon 2017. Autoware.ai is based on ROS 1, which has certain fundamental design decisions that make it impractical for production autonomous cars. ROS 2, backed by Open Robotics, Intel, Amazon, Toyota and others, is quickly maturing, and from the very beginning was designed to fulfill the needs of not only researchers in academia, but also the emerging robotics industry.

Autoware.Auto launched in 2018 as an evolution of Autoware.AI, based on ROS 2, applying engineering best practices from the beginning, such as documentation, code coverage and testing, to build a production-ready open-source stack for autonomous driving with the guarantees in robustness and safety that the industry demands. We want to modularise Autoware.ai and align with Autoware.Auto and move to ROS 2.

We want high quality software, we care about safety and we want to do things right. Parkopedia’s main contributions so far have been to improve the quality of the code by fleshing out the CI infrastructure, adding support for cross-compiling for ARM and the NVIDIA Drive PX2, modernising the message interfaces and developing a new driver to support 8 cameras, among other improvements.

Our plan for 2019 is to keep contributing to Autoware.AI and Autoware.Auto to support the StreetDrone ONE and to make whatever changes necessary to support our Autonomous Valet Parking demonstration.

We’re very grateful to Shinpei Kato and the Tier4 team for open-sourcing Autoware and for welcoming our contributions.

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