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:
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,
The vehicle exiting the parking spot and driving to the pick-up zone (where the vehicle’s regular driver would collect it),
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.
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, 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?
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:
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.
Utilises parking spaces more efficiently by tighter/double parking of autonomous cars, and optimally distributing these vehicles within the available parking real estate.
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.
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
Developing automotive-grade indoor parking maps, required for autonomous vehicles to localise and navigate within a multi-storey car park.
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.
Demonstrating this self-parking technology in a variety of car parks.
Developing the safety case and preparing for in-car-park trials.
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.
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.