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Automatic Maneuver Recognition in the Automobile > 11. April 2023 > Folie 1Institut für Verkehrsführung und Fahrzeugsteuerung > Technologien aus Luft- und Raumfahrt für Straße und Schiene

Automatic Maneuver Recognition in the Automobile:

the Fusion of Uncertain Sensor Values using Bayesian Models

Arati Gerdes


Overview

1. Problem Description

2. Challenges

3. Dealing with Uncertainty

4. The Experiment

5. Results

6. Current Work



2. Challenges


4. The Experiment

5. Results

6. Current Work


Early Automobiles

Karl Benz’s Tri-Car (1885) Henry Ford’s Quadricycle (1896)


Automobiles Today


Driving-Maneuver Recognition

Fuse the available sensor data in order to identify the driving maneuver being performed

Understand the driver‘s intentions

Tune assistance to the driver‘s needs



2. Challenges


4. The Experiment

5. Results

6. Current Work


Challenges to Automatic Maneuver-Recognition

Inaccuracy in the sensor values:

Today‘s automobile sensors return uncertain and often invalid values

Driving maneuvers are performed in a wide variety of driving styles

It is difficult to define a set of rules that describe a maneuver

Rule-based systems are inadequate for maneuver-recogition




4. The Experiment

5. Results

6. Current Work

2. Challenges


Probabilistic Inference using Bayesian Networks

Solid, theoretical framework of Probability Theory

Derivation of inferences from uncertain evidence

Directed Acyclic Graph

Nodes represent variables of interest

Edges represent dependencies

Strengths of the dependencies are described by local conditional probability tables

Merge Left

Indicator Steering

Merge left

FALSE

Merge left

TRUE

Indicator OFF

0.9 0.2

Indicator ON

0.1 0.8


Probabilistic Inference using Bayesian Networks

Supports Bidirectional inference

Supports Learning



4. The Experiment

5. Results

6. Current Work

2. Challenges



The ViewCar


Ten Maneuvers Selected for Recognition

Maneuvers

Stopped

Follow road

Follow car

Turn left

Turn right

Merge left

Merge right

Overtake

Enter freeway

Exit freeway



5. Results

6. Current Work

2. Challenges


4. The Experiment


Prototype System and Experimental Results

A Prototype System was built and tested with data from the ViewCar

40 Minutes, 152 Maneuvers

Recognition confidence threshold set to 60%

140 of the maneuvers (~92%) were recognized by the system

The correct maneuver was recognized during approximately 77% of the drive duration

A false maneuver was recognized during approximately 14% of the drive duration.

An unrecognized maneuver was returned during approximately 9% of the drive duration.


Prototype System and Experimental Results

Maneuver Number driven

Number recognized

Stopped 2 2

Follow Road 48 48

Follow Car 41 40

Turn left 5 4

Turn right 3 3

Merge left 11 10

Merge right 23 19

Overtake 13 8

Enter freeway 2 2

Exit freeway 4 4



6. Current Work

2. Challenges


4. The Experiment

5. Results


Current Work

Construction of an online version of the system

Construction of more detailed models to improve recognition accuracy

Incorporation of input from long-range radar

Correlation of driver gaze behavior with driving intent to add predictive power


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Institut für Verkehrsführung und Fahrzeugsteuerung > Technologien aus Luft- und Raumfahrt für...

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