Month: January 2019

Autoware

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.

Risk Assessment and Method Statements (RAMS)

One of the key objectives of this Autonomous Valet Parking project is to demonstrate our autonomous vehicle parking itself in a covered car park. The Transport Systems Catapult is responsible for the safety work package which ensures that all activities undertaken during the project are done in a systematic and safe manner. One of the important deliverables to ensure safety is the Risk Assessment and Method Statement (RAMS).

The RAMS document generally includes:

1.       An overview of the project and key objectives to provide the reader with a background of the project

2.       The activity being assessed, including:

  • Roles and responsibilities
  • Limits of the operation and trial details (route planned, scenarios, vehicle specifications, time of day, limits, weather, specifications)
  • Legal considerations such as vehicle insurance and laws
  • Emergency procedures (eg. vehicle breaking down, network error, sensor malfunction, accident)
  • Training achieved (eg. driver training on the StreetDrone.ONE vehicle, taking over manually)

3.       A risk assessment listing hazards, consequences, mitigation methods and detailing who might be harmed. Following the ISO 26262 standard, a hazard analysis and risk assessment is required in order to determine the criticality of a system.

The risk analysis is focused on:

  • Risks related to the ongoing operation of the vehicle
  • Risks related to the operation of external factors that affect current operation
  • Risks arising from the new equipment that may affect the safety of the vehicle or other
RISK MATRIX (To generate the risk level)
ACTION LEVEL (To identify what action needs to be taken)

The method statement part describes in a logical sequence how a task will be carried out in a safe manner. It includes all the risks identified and the measures needed to control those risks.

The purpose of the method statement is to ensure that:

  • The trial is carried in a structured, controlled and safe manner
  • The hazards and associated risks are understood and mitigated

While the ultimate goal of the project is to demonstrate Autonomous Valet Parking, we will build up to this demonstration through smaller manageable steps and a separate RAMS will be produced at each stage:

1.       Capturing data in car parks

2.       Testing in a controlled environment

3.       Testing in a covered car park

4.       Demonstration

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