enhancement of radar tracking with low level radar …

ENHANCEMENT OF RADAR

TRACKING WITH LOW LEVEL

RADAR DATA

Month 11, Year 2019 Niclas Carlström

In order to get a sense of a vehicle’s surroundings

radar data is used to create objects around a

vehicle. Today these objects are created by a

series of algorithms based on detections from the

radar in a so called tracker.

Background

2

Tracker view

Object/track

Radar field of view

Standard view

A detection is a single radar reflection point that

contains information about the range and angle to the

object the detection came from.

Background

3

Detection

Detection view

Angle

Range

Detections are typically formed by performing Fast

Fourier Transforms (FFT) on raw data from the radar.

The measured reflection of a target is however not a

single point but a radar wave reflecting on an object.

This refection contains a large amount of data, and

with it a lot of information.

Background

4

Radar reflection view

Radar reflections

Radar waves

By analyzing and processing the raw data differently,

it could be possible to improve the radar tracker

performance greatly.

Purpose

5

Object size estimation

By using the object output from the tracker and reinserting them into

the CDC data, one could focus algorithms on certain areas in the

data cube and also predict where object information should be in the

CDC domain. This could potentially be used for finding the velocity

profile of an object, or to detect yawing of an object if it diverges from

a linear movement. Most importantly, size estimation could possibly

be done by analyzing the energy between detections that is expected

to stem from the same object.

Detection clustering

By grouping together detections at CDC level data, useful

estimates and assumptions could later be done within the tracker

algorithms. This could be done by exploring how the information

between detections look like, using knowlede such as how bushes,

walls and guardrails behave within CDC level data. Here eithier

algorithms could be developed, or machine learning be

implemented in order to reach saught after results.

Scope

6

When looking into low level radar data, also known as CDC data, with the purpose to enhancing radar tracking performance, there are

many areas to explore. APTIV would however like to focus of either of the following; Detection clustering and/or object size estimation.

Driver model based trajectory planning | 5/11/19

Contact Information

Master Thesis: Ground Truth Estimation | 4th of November, 2019 | Aptiv Confidential7

Niclas Carlström

[email protected]

mailto:[email protected]

RADAR ONLY

ESTIMATION OF EGO

MOTION

Month 11, Year 2019 Hanna Nyqvist

Measured range rate from radar

Radar Only Estimation of Ego Motion | 2019-11-04 |

Aptiv Confidential9

Host over the ground speed

Measured relative range rates by radar

Objective:

• Estimate ego velocity from radar measurements only

• Algorithm must be robust against outlier radar measurements that comes from non stationary objects

• Investigate how to estimate also ego yaw rate

• Investigate under which conditions algorithm converges (e.g. number of detections needed, maximum outlier to inlier

ratio)

Extra objectives:

• Investigate fusion of radar only estimates with other information sources such as wheel speed sensors/IMU etc

Data:

• Real data from traffic driving

• Real data from test track with ground truth from differential GPS

Contact person: Hanna Nyqvist

Scope

Radar Only Estimation of Ego Motion | 2019-11-04 |

Aptiv Confidential10

Contact Information


Hanna Nyqvist

[email protected]


GROUND TRUTH ESTIMATION

FOR VALIDATION OF RADAR

TRACKING

Month 11, Year 2019 Elvira Ramle

In order to get a sense of a vehicle’s surroundings radar

data is used to create objects around a vehicle. Today

these objects are created by a series of algorithms based

on detections from the radar in a so called tracker.

The purpose of a radar tracker is to take input from one or

several radar sensors in the form of detections and

produce reliable and true estimates of the states of

surrounding vehicles and objects. Different states are

estimated such as the velocity, heading and position and

this is done in various, and often complicated, traffic

scenarios.

Background

Presentation Title | Date | Aptiv Confidential13

Object/track

Radar field of

view

Standard view Tracker view

Purpose


There are multiple tools to use when testing the performance of

the tracker:

• High precision GPS data

• LiDAR sensors

• Camera systems

However, these systems are either hard to employ in real traffic,

expensive or unreliable. Therefore, a tool that can produce better

estimates than the tracker in an offline environment would be very

useful for testing and validation of the tracker performance. This

would not only save time, but could help improve the tracker

performance significantly.

The idea is to develop an algorithm for estimation of ground truth

that can be used for test and comparison of the tracker itself. The

algorithm will utilize radar detections as input, like the tracker

itself. Additionally, it will also be allowed to use “future” data in its

estimation in contrast to the tracker which only has access to the

past and present.

Scope


Two different approaches

• Ground Truth Estimation: Specific Traffic Scenarios

• Focus on specific traffic scenarios where the availability

of ground truth data would be most useful

• Examples: Traffic Jams, Tunnels, EuroNCAP test

cases etc.

• Ground Truth Estimation: General Approach

• Algorithm development where no general assumptions

can be made on the behavior of surrounding traffic

Contact Information


Elvira Ramle

[email protected]


DRIVER MODEL

BASED TRAJECTORY

PLANNING

November 5, 2019 Joachim Rukin

Trajectory planning is an essential part of an autonomous driving system. Let it be a lane

following function or a fully automated system, the trajectory planning provides input to the

motion control in order to get from point A to point B in an optimal way.

In lane following systems, the center of the lane is often calculated with the help of vision

systems and then tracked to ensure that the vehicle stays safely on the road. However, this

behavior of always following the center of the lane is something that differs from how a

normal driver would position the vehicle on the road, especially when driving in curves.

This unnatural positioning can feel uncomfortable or unsafe for the driver. The planning

algorithm need to be designed in a fashion so that it:

• Ensure that the planned trajectory keeps the vehicle within the lane bounds.

• Ensure that the planned trajectory can be executed comfortably for the driver.

• Can perform biasing maneuvers in lane to further increase the comfort level for the driver.

• Is computationally efficient to be able to run in real time.

Background

Driver model based trajectory planning | 5/11/19 18

Identified lane center for country road

• Planned trajectories feel more natural to the driver which increases the trust in the system.

• As multiple different driver models exist, dedicated research is required to arrive at the best solution.

• Since the trajectory has been planned with requirements concerning safety and comfort, the overall ride for the driver will both be safer

and more comfortable.

Purpose


Using Aptiv’s existing test vehicle platform, a trajectory planning algorithm utilizing a driver model needs to be created. The developed

algorithm should be able to comfortably and safely plan trajectories to execute a set of desired maneuvers for the host vehicle. One such

set of maneuvers could be

• Lane following

• In-lane biasing

• Minimum risk maneuver, such as stopping by the side of the road

• Lane changing

Scope


Contact Information

21

Joachim Rukin

[email protected]



AUTONOMOUS

TRAILER PARKING

June 24, 2019 Andreas Andersson

Autonomous Trailer Parking | 2019-06-25 | Aptiv

Confidential23

Objective:

• Develop a path planner for parking a truck-trailer vehicle in a dense parking lot.

• The result could potentially be visualized in e.g. TruckMaker using some existing lateral and longitudinal controller.

• Focus path planning, not controller

Data:

• Map of parking lot and ego vehicle position

Contact person: Karin Brötjefors

Scope


Confidential24

Traditional path planning algoritms

from the A* family could be

used under constraints from a

motion model of an

articulated vehicle. Reverse parking

could also demand dividing the path

into two parts, which needs to be

connected at a position with a

common pose.

Model-based path planning methods


Confidential25

There is an area of machine learning that

is called Reinforcement Learning where

software agents take actions in an

environment based on some

reward function. Using this method, the

trailer could learn how to park by

formulating a higher reward for

approaching the parking space, and a

lower reward for colliding with obstacles or

driving in the wrong direction.

Machine Learning


Confidential26

Contact Information

27

Andreas Andersson

[email protected]




INTELLIGENT

HIGHWAY MERGE

PILOT

June 24, 2019 Andreas Andersson

Intelligent Highway Merge Pilot | 2019-06-24 | Aptiv

Confidential29

Objective:

• Feature to do ”human like lane change” on highway when another vehicle must merge into the ego vehicle’s vehicle’s

lane

• Closed loop simulation in CarMaker, i.e. Lateral and longitudinal control

• Focus trajectory, not controller

Data:

Objects and lanes are present as processed output

Contact person: Anton Öqvist

Scope

Intelligent Highway Merge Pilot | 2019-06-24 | Aptiv

Confidential30

Contact Information

31

Andreas Andersson

[email protected]




MULTI-AGENT

SYSTEM SIMULATION

OF ACC SCENARIOS

October 25, 2019 Anton Öqvist

Multi-agent system simulation of ACC Scenarios

Multi-Agent Simulation of ACC Scenarios | 2019-10-25 |


Objective:

• Multi-agent system that creates realistic traffic scenarios with group behavior affected by road properties like hills

• Agent models with one degree freedom and variants to models behavior of different driver types and reaction patterns

• Possibility to switch individual agent behavior to an ACC

• Closed loop simulation in Matlab/Simulink or Car Maker where all agents act autonomously

Extra objectives:

• Add other road more road properties that might affect agents like speed limits

Data: Simulated in Matlab/Simulink with autonomous driving toolbox or Car Maker


Scope

Multi-Agent Simulation of ACC Scenarios | 2019-10-25 |


Contact Information

35

Anton Öqvist

[email protected]



MULTI-AGENT

SIMULATION OF AEB

EVENT

October 25, 2019 Anton Öqvist

Multi-Agent Simulation of AEB Event

Multi-Agent Simulation of AEB Event | 2019-10-25 |


Objective:

• Multi-agent system that creates realistic AEB scenarios with multiple road users

• Agent models with one degree freedom and variants to models behavior of different driver types and reaction patterns

• Closed loop simulation in Matlab/Simulink or Car Maker where all agents act autonomously

Extra objectives:

• Possibility to run simulation with an active AEB in some or all agents

• Extending the model to react to warnings from AEB

Data: Simulated in Matlab/Simulink with autonomous driving toolbox or Car Maker


Scope

Multi-Agent Simulation of AEB Event | 2019-10-25 |


Contact Information

39

Anton Öqvist

[email protected]



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