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School of Electronic, Electrical and Systems Engineering

A Scalable and Modular Architecture as a

Research Platform for Driverless Cars 1. Abstract

The aim of this project was to develop a research

platform for driverless cars at Loughborough

University. A scalable and modular architecture was

implemented, facilitating supervisory control and

elements of autonomy including road sign

recognition, path planning and obstacle avoidance.

These components were integrated into a complete

system and verified successfully as a proof of concept

to be harnessed in future research. Reduced latency

and greater reliability could be achieved with

additional input/output hardware and more extensive

testing. Concept illustrated in Figure 1.

2. System Architecture

• The architecture was required to be modular

thereby ensuring readability and scalability for

future work.

• Based on research in mobile robotics, a layered

approach was taken which abstracts system

components depending on whether they address

short or long-term goals, Figure 2.

3. Hardware Implementation

• A Track Based Vehicle (TBV) formed the core of the

system, Figure 4. A base plate was connected on

top for attaching other sensors and systems.

• Supervisory Control was hosted on a laptop

external to the TBV.

• The Executive and Functional layers directly

influence the safety of the vehicle and require high

determinism. As such, the NI cRIO was selected

due to its Real-Time operating system and FPGA

capabilities.

• The Vision Processing was performed on a

dedicated NI Industrial Controller. This could cope

with the high demands without affecting other time-

critical parts of the system.

• For close range obstacle avoidance 200mm –

1000mm, Sharp 2Y0A02 Infrared sensors were

fitted at all four corners of the TBV.

• At medium range, the Hokuyo URG-04LX Lidar

sensor forms the main sensing component. This

maps the horizontal plane of 240° in front of the

TBV at a range of 60mm – 4095mm.

4. Software Implementation

• Using NI LabVIEW, a software implementation of

the architecture was developed. LabVIEW’s

graphical nature enhanced productivity and aided

in developing modular, readable and scalable code.

Furthermore, LabVIEW was able to be used across

all hardware platforms; reducing complexity,

incompatibility and allowing solid integration.

• Supervisory Control was programmed first,

allowing manual TBV control and providing

feedback in the form of sensor data and a video

feed. This was to ensure a safe mode of operation

exists should a failure occur in the autonomous

mode.

• The Autonomous Driving state in Figure 5 includes

additional processing of the sensor measurements

to produce motor commands with minimal human

interaction. A vector field histogram is applied to

identify obstacle free paths which can be followed

by specifying a desired heading.

• The Vision Processing component was developed

to identify and track road traffic signs using

Geometric Pattern Matching. Optical Character

Recognition (OCR) was then used to read sign text

for speed limit detection.

5. Conclusions and Future Work

• All deliverables have been met as a proof of concept with examples shown in Figure 6; however, limitations

exist in terms of system latency from network communications. With additional hardware, the FPGA chassis

could be better utilised as the Functional Layer, closing the loop between sensors and actuators in hardware

and reducing the latency.

Jamie Martin Jones - B021956

MEng Electronic and Electrical Engineering

Supervised by Prof. Roy Kalawsky

Figure 1 - Driverless Cars Concept [1] Figure 2 - Architecture Design

Figure 3 – NI

CompactRIO [2]

Figure 4 – Track Based Vehicle Figure 5 – Executive Software (Autonomous State)

References:

[1] (2012, December 13). Cars Coming Soon: Volvo's "Crash Proof" Car. Available: http://blog.cargurus.com/tag/driverless-cars, Last Accessed 6th May 2015.

[2] (2015). NI CompactRIO Advisor. Available: http://ohm.ni.com/advisors/crio/pages/common/intro.xhtml, Last Accessed 6th May 2015.

• Future work could expand on this platform with

modules such as GPS Navigation or Self-Parking.

Finally, this platform could be scaled up to a full

self-driving road vehicle and enable

Loughborough University to lead UK research in

this exciting field.

Figure 6 – Verification of Sign Recognition and Path Planning