tata motors autonomous vehicle architecture autonomous ... · tata motors autonomous vehicle...

Tata Motors Autonomous Vehicle

Architecture

International Connected

Autonomous Vehicles

29th January 2019

Dr. Mark Tucker

Copyright, Confidential, Tata Motors Limitedc

• Introduction

• Tata/TMETC

• UK Autodrive Project and Trials• TMETC Autonomous Hexa Architecture

• Functional/Hardware/Software• Autonomous Functions

• Sensor locations

• Perceptions

• Planning - Global/Behaviour/Trajectory

• Motion Control/Drive-by-Wire Actuation • Conclusion

Overview


• Operations in more than 100 countries• 660,000 employees• Tata Group $100bn turnover• Tata Motors $42bn turnover

Tata Group

Tata Motors European Technical Centre

• Created 2005• Based in Coventry• Wholly-owned subsidiary of Tata Motors• Research & development principally for Tata Motors• Engineering Centre, Design Studio, Workshops• 180-strong workforce


• Project objectives:

• Demonstrate autonomous and connected vehicles (V2X) in real-world urban environments

• Provide insight for stakeholders including legislators, insurers and investors• Vehicles:

• Pods (RDM) in Milton Keynes

• Autonomous (TMETC and JLR)

• Connected (TMETC, JLR and Ford)• Duration

• 3 Years: November 2015 – October 2018• Funding

• Part funding from Innovate UK (around £10m of £19.2m)

UK Autodrive Project

UK Autodrive Trials - Milton Keynes October 2018


Sensing

Gather data from surroundings

Perception(and localisation)

Interpret sensed data

Planning

Decide what to do

Control and Actuation

Achieve Desired Trajectory

Autonomous Functional Architecture

LinuxROSC++ Python

WindowsPTP/CANMATLABSimulink


Autonomous Hardware Architecture


Open Source Robot Operating System (ROS) Bus

GPS/SLAM

Location LocationGlobal Plan

WatchdogPoint Cloud

Rviz

Trajectory Planner

Map

Locatin

1: Sensing

Gather data from surroundings

2: Perception(and localisation)

Interpret sensed data

3: Planning

Decide what to do

4: Control

Achieve Desired Trajectory

Sensors

Sen

sor

Dat

a

GPS/SLAM

Loca

tio

n

Global Planner

Glo

bal

Pat

h

Loca

tio

n

Map

Behaviour Planner

Loca

tio

n

Beh

avio

urs

Fuse

d O

bje

cts

Glo

bal

Pat

h

Trajectory Planner

Loca

tio

n

Fuse

d O

bje

cts

Beh

avio

urs

Loca

l gri

dm

ap

Traj

ecto

ry

Sensor Fusion

Fuse

d O

bje

cts

Sen

sor

Ob

ject

s

PointcloudProcessing

Loca

l gri

dm

ap

Lid

ar D

ata

Control GUI

Co

mm

and

s

All

Stat

us

System

Traj

ecto

ry

RViz

Sen

sor

Dat

a

Watchdog

All

Stat

us

StatusSpeedgoat

Motion Controller

CA

N

Drive-by-Wire

ROS Bridge

Trajectory

Status

MATLAB/Simulink

Linux/C++/Python

Traj

ecto

ry

Loca

tio

n

Supplier

Autonomous Software Architecture


Sensor Locations

Sensor Type

Radar

Radar

Radar

Radar

Radar

Ibeo LIDAR

Ibeo LIDAR

Ibeo LIDAR

Ibeo LIDAR

Ibeo LIDAR

Ibeo LIDAR

Velodyne LIDAR

Velodyne LIDAR

Mobileye

Camera

Camera

Camera

CameraGPS

AntennaGPS

Antenna

LIDAR RADAR Vision GPS


Mapping

Pointcloud processing

Mono camera object and lane detection

Sensor Fusion

ClassificationSimultaneous Localisation and Mapping (SLAM)

Localisation and Mapping (LOAM)

Perception


Rviz – ROS Visualisation


PlanningGlobal Planner• Finds the optimal route to the

destination (according to some time/cost objective)

Behaviour Planner• Static (strategic) behaviours from

the map e.g. keep in lane, stop, give way, left turn

• Dynamic (tactical) behaviours in response to the environment e.g. traffic lights and objects (and evasive trajectories to mitigate risk)

Trajectory Planner• Generate multiple obstacle free

paths• Assign speed profiles

• Select trajectory • within lane boundaries,

avoiding dynamic/stationary obstacles, comfortable (yaw rate; lateral and longitudinal acceleration and jerk; meets regulatory constraints (speed limits, stop lines, traffic lights etc)

• Select evasive trajectory


• Functions

• Longitudinal control – demand the throttle position and brake pressure to achieve the trajectory plan speed profile of the selected target trajectory

• Lateral control – demand steering position to follow the path of the target trajectory plan• State management –

• Return control to the driver on system errors (based on fault analysis e.g. message delays, incorrect data, drive-by-wire faults)

• Emergency or target trajectory selection• Auxiliary control – command (lights, indicators, hazards, front/rear wipers)

Motion Control/Drive-by-Wire Actuation

Motion Control

Drive-by-Wire


• Successful completion of trials • Pragmatic approach to Autonomous Vehicle Development

• Open source middleware – ROS

• Off-the-shelf Tools

• Linux/C++/Python/Rviz/CANalyser/MATLAB/Simulink

• Off-the shelf hardware

• Radar/lidar/GPS/IMU/cameras

• Industrial PCs/Speedgoat/Drive-by-wire mobility solution

• Bespoke third party software

• Speedgoat bridge/Sensor Fusion

• TMETC Software

• Perception/planning/motion control

Conclusion

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