Connected Places Catapult (CPC) have conducted research with
UK car drivers and stakeholders to better understand public and industry readiness
for autonomous valet parking (AVP).
The key questions guiding the research were:
What are the key parking pain points that can be
resolved by AVP?
What are other likely benefits of AVP to users
and parking stakeholders?
What are the key barriers to AVP deployment and
uptake, from a social and behavioural point of view?
What will be the likely impact of AVP, on the
environment, the economy, and the parking industry?
The report produced details the findings, conclusions and
recommendations from a suite of research activities:
A literature review to explore existing
knowledge about the chosen research topics and questions;
Stakeholder interviews with parking
professionals and OEMs to explore their views of AVP;
Focus group interviews to explore the needs and
attitudes of drivers in-depth; and
A UK wide survey of 1025 car drivers to examine
differences between user groups, and to gauge how common certain attitudes or
The research found that car drivers would be more receptive
to the car taking control in a car park than on the roads and a technology
solution that can reduce the stress of parking and make parking easier would be
appealing. One in five drivers would like to use AVP now with a further 40%
open to the idea but wanting to know more about it. Likely early adopters would
be younger male drivers and those who have previously used driver assistance
technology or have previously used a valet parking service.
We successfully completed our first tests at Turweston Aerodrome last week.
The plan was to check and ensure the robustness of the drive-by-wire system, to train our safety drivers and to do basic path following.
We also took the opportunity to collect some data from the ultrasonic sensors that are on the StreetDrone.ONE which we will use for system safety.
For testing the drive-by-wire system, we carried out a number of test runs using teleoperation from the driver. We drove on the track forward, backward and changing steering at various speeds. The system performed satisfactorily. We also performed a full brake test to work out a safe driving speed and stopping distance in a case of emergency stop. Further details are presented in this blog post.
On the final day, we tested a basic path following to make sure everything worked together. We integrated the drive-by-wire (dbw) system with a path follower, PID motion controller and a basic gps and imu localisation in this open space environment.
We managed to achieve the objective of testing the dbw with the feedback control for the path following. However, precision was not there as we expected. The basic imu and gps sensor localisation would not give very accurate positioning and tends to drift away or jump around to within 5m accuracy. To resolve this issue, we are working on a better localisation using RTK GPS (like a simpleRTK2B) using RTK corrections over NTRIP.
Safety is the highest priority at the Autonomous Valet Parking project. As we seek to demonstrate that Parkopedia’s maps are suitable for localisation and navigation within covered car parks, our safety case must ensure the safety of the people, vehicles and infrastructure inside our test vehicle and without. There have been some highly publicised incidents involving other organisations’ autonomous vehicles over the past year or two, so what is the AVP project doing around safety?
Our Safety Case involves a combination of System Safety and Operational Safety, to achieve the required assurance levels around our activities. System Safety covers any and all aspects of the system (hardware, software or both) that contribute to the safety case and is the focus of this post. Operational Safety is all the operational decisions taken to ensure that we are conducting the tests safely and you can read more about that here.
Our test vehicle, a StreetDrone.ONE is a converted Twizy, and comes with ultrasound range sensors. There are eight Neobotix ultrasound sensors in total, three to the front, three to the rear, and one in each door looking sideways. The front sensors are slightly fanned out, and in the rear the outer two are corner mounted. The signals from each sensor are gathered together by a Neobotix board and this publishes the range data as an automotive industry canbus signal. These can be monitored and action taken when a significant measurement is made.
The sensors create a “virtual safety cage”. This virtual safety cage can be imagined as an invisible cuboid around the StreetDrone.ONE, slightly wider and longer than the car itself. If anything, be it a car, pedestrian or wall, intrudes inside this cage, the car should stop immediately, thus acting as a belt and braces to the perception and navigation parts of the AI driving the car.
The video below shows a demonstration of the drive-by-wire system of the StreetDrone at 14 mph. The brake is applied by the actuators at the very beginning of the 3 metres wide white strip. Based on a 14mph start speed, we calculated the braking distance to be 4.5 metres. This is obviously an approximation, and the actual braking distance and time depends on many factors including brake disk wear and tear.
Remembering our high-school equations of motion:
Where “v” is the initial velocity, “u” the final velocity, “a” is the acceleration and “s” the distance.
Now we can rearrange this to obtain the acceleration, remembering in our case the final velocity is zero:
These numbers match well with the similar experiments carried out by StreetDrone using IMU data, which have shown peak deceleration of 0.67g and an average deceleration of 0.46g.
The maximum range of the Neobotix ultrasound sensor is 1.5 metres, so we could do the above calculation in reverse and calculate the maximum safe speed. Allowing for a safety buffer of 0.5m:
The distance “s” is 1.0 metres, the acceleration “a” is 4.3m/s^2, and again the final velocity “u” is 0 m/s.
The present AVP plan calls for a maximum speed within car parks of 5 mph which is well within the safety margin calculated above. The next step now is to process the data we’ve captured from the ultrasonic sensors and to develop the software that will automatically apply the maximum brake whenever the virtual safety cage is breached.