A Case Study of Autonomous Vehicle:

AutoValetDr. Benjamin Ma

Automotive Summit Bangkok

Jun/2019

Agenda

▪ Introduction and Background: the autonomous vehicle program

▪ Auto-parking in modern ADAS

▪ Motivation and Objectives

▪ Approach and Methodology

▪ Commercial application

▪ Results

▪ Q & A

2

Smart Nation: Smart Urban Mobility Initiative

▪ Strategic National

Projects that drive

pervasive adoption of

digital and smart

technologies throughout

Singapore, including

healthcare, living,

mobility and service.

3

Smart Nation: Smart Urban Mobility Initiative

4

▪ Autonomous vehicle technology

is the key enabler to realize

“Smart Urban Mobility”

▪ Smart Urban Mobility is not just

about autonomous vehicle.

▪ Connectivity, shared, assistive

and green

Autonomous Mobility Initiative

5

Gov.

AcademiaIndustry

Autonomous Vehicle in Singapore

6

Singapore Autonomous Vehicle Initiative (SAVI)Joint partnership between LTA, JTC and A*STAR to provide

a technical platform for industry partners and stakeholders

to conduct research and development (R&D) and test-

bedding of AV technology, applications and solutions.

7

Background: a bit of history

▪ Toyota COMS Electric Vehicle

▪ Modified for drive-by-wire/steer-by-wire

8

Background: a bit of history

System Integration: 6 months

3D SLAM on a robot

9

Background: Progress

▪ 3D mapping and localization

▪ Planning

▪ Perception

▪ Longitudinal (velocity) and lateral (steering) control

▪ Sensor fusion

▪ Campus navigation

10

What’s Next?

▪ No COE, no R&D license plate

▪ Not road ready

▪ Autonomous vehicle test site: Fusionopolis and NTU CleanTech park

▪ Limited resources and change in focus area

11

What’s Next?

▪ Drivers in London typically spend almost eight minutes searching for somewhere to park after each journey.

▪ People in KL Waste 25 Minutes Daily Just to Look for Parking Spots.

▪ People even spend more time walking from carpark to their places.

▪ Auto-parking: Driverless Valet Parking, Automated Valet Parking

Telegraph.co.uk

12

Auto-Parking in Modern Commercial ADAS

▪ First commercial auto-parking: Toyota Prius, 2003, automatic parallel parking function (Intelligent Parking Assist)

▪ 2006, Lexus LS, parallel and angle parking

▪ After 2009, Ford, BMW, Audi, etc.

▪ One of the option of modern ADAS

▪ Passive (parking assistance) and Active types (auto-parking)

The British Toyota Prius with Intelligent

Parking Assist has a dashboard screen

to tell the driver what to do.

13

Auto-Parking in Modern Commercial ADAS

▪ First commercial auto-parking: Toyota Prius, 2003, automatic parallel parking function (Intelligent Parking Assist)

▪ 2006, Lexus LS, parallel and angle parking

▪ After 2009, Ford, BMW, Audi, etc.

▪ One of the option of modern ADAS

▪ Passive (parking assistance) and Activetypes (auto-parking)

▪ Human-in-the-loop

Front

Sensor

Rear

Sensor

Side Sensor

14

▪ To develop an auto-parking system including hardware, software and algorithms.

▪ The auto-parking system must be integrated into existing auto-driving framework

▪ The car must be able to locate, navigate to and park itself into the carpark without human intervention. (human-not-in the-loop)

▪ Avoid any static/dynamic obstacles during parking

▪ Scenario: driver simply choose a carpark lot and the car shall be able to park by itself (end-to-end).

Motivation and Objective:

15

Approach and Methodology

▪ Vehicle Modelling

▪ Path planning

▪ Integration of auto-parking into existing autonomous driving framework

▪ Ackermann steering geometry

16

Vehicle Modelling:

l

o i

𝑟

Centre of

turning circlei

m

𝑙

i

Front-back wheel centre distance

(wheelbase)

i Actual vehicle steering angle

Turing radius

m

sa

𝑟

𝑙

Left (outer) wheel orientation angle

Right (inner) wheel orientation angle

Left-right wheel centre distance (track)

▪ Bicycle Model

Centre of

turning circle

𝑟

𝑙

i

i

= tan-1( l/r)o

Steering angle

Wheelbase

Turning radius

17

Width 1.40m

Length 2.406m

Height 1.505m

Steer angle [-660,660]

degree

Steering ratio 21.3

Front wheel angle [-31,31]

degree

Physical property of our NYP car

Vehicle Modelling:

v:

Angular velocity

Linear velocity

w:

(Kinematic

constraint 1)

r >=2.55minimum turning radius

|v/w|>=2.55

(Kinematic

constraint 2)

Equation of

steering angle

conversion

Used in Path Planning part Used in Control part

sa

𝑙

Vehicle model structure

= tan-1(l*w/v)sa

Derive

r =v/w

Angular velocity

Linear velocity

Turning radius Steering angle

Wheelbase

Turning radius

sa = tan-1( l/r)

Equation1: Equation2:

18Timed-Elastic-Bands

Path planning development:

1. Subject to kinematic constraints

2. Planning for car-like robot/mobile base

3. Ability of avoiding dynamic obstacles

4. Human-like behavior for auto-parking path

5. Fast

Requirements of path-planner for car:

1. Search-based: A*, RRT, probabilistic roadmap

2. Reward-based: potential field

3. Sampling-based: SBLP

Motion/Path planning methods

Elastic Band Planning Methods

1. Use global planner to plan a global path (A*,

D*, etc.)

2. Apply two forces: an internal contraction and an

external repulsive force to modify the global

path like an “elastic band”

3. Dynamic obstacles, kinematic constraints,

environmental constraints can be

configured/encoded in the “force”.

19Timed-Elastic-Bands

Elastic Band path planner

Elastic Band Avoid an obstacle

This path should satisfy our car kinematic constraints:

• R>=2.55m• v/w>2.55m• Wheelbase=1.53m

20

Simulator

Setup of simulation environment:

Visualized carpark

Car FootprintLaser scan

Car

Obstacles

Other parked cars

Visualization of path-planning and control

Planned path

21

Desired planed path Undesired planned path

Problem revealed in simulation

Conclusion: Elastic Band planner can’t always plan a desired path for auto-parking

(Human-like)

22

First position: plan a path to navigate the car close

enough to the parking lot

Second position: plan a path for the car to park

inside the parking lot

Addressing the problem: Divide-Conquer Strategy(Just like human)

Computing the first position: trajectory rollout

Roll out a trajectory (kinematic equations) from the second

position to the first position for checking collisions

Increase the turning radius of projected trajectories

until there is no collision and minimum clearance

Sample a random points from the candidate points

No

collision

Obstacle

24

Approach and Methodology

▪ Vehicle Modelling

▪ Path planning

▪ Integration of auto-parking into existing autonomous driving framework

▪ Costmap

▪ ROS concept for space quantization and motion planning

▪ Global and local

▪ 2D/3D

▪ Configurable: inflation, update rate, etc.

▪ Carpark lot representation in map

▪ Assumption: carpark location is fixed in the map

▪ Size: proportional to the Toyota COMS electric vehicle

27

Elastic

Band

Planner

Linear

Velocity input

Angular

Velocity input

Throttle commandBrake command

Steer command

PID velocity

control

Steering angle

control

Vehicle controller

(Actual velocity feedback)

(Actual steering angle feedback)

= tan-1( l*w/v)saFrom Vehicle Modelling part:

How was it integrated into a driverless car?

Longitudinal

Lateral

Localization

Perception

costmap

vehicle

6 DOF state

General

Navigation

Planner

28

Commercial application: Valet auto-parking

Integrate autonomous navigation and auto-parking into one framework

29

Thank you!!

30

31

Some outreach and public demos

ITS Summit 2017 LEAP Exhibition 2017