AI in Air Traffic Management Christian Thurow, Head of R&D at - PowerPoint PPT Presentation

AI in Air Traffic Management Christian Thurow, Head of R&D at Searidge WWW.SEARIDGETECH.COM/AIMEE

Motivation 1/3 What is Air Traffic Control? 2

Motivation 2/3 Work Increase • Annual Growth: 6-7% • both #passengers and #flights • 2016: 3.7b passengers worldwide Source : International Civil Aviation Organization, Civil Aviation Statistics of the World and ICAO staff estimates. 3

Motivation 3/3 4

Our Goals 5

Our Goals • reduce controller workload 5

Our Goals • reduce controller workload • increase situational awareness 5

Our Goals • reduce controller workload • increase situational awareness • declutter workspace 5

Our Goals • reduce controller workload • increase situational awareness • declutter workspace • provide additional surveillance data source (added safety) 5

What does Searidge do? 6

What does Searidge do? 6

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Challenges 8

Challenges • Building the NN 8

Challenges • Building the NN • Training 8

Challenges • Building the NN • Training • Inferencing Speed 8

Challenges • Building the NN • Training • Inferencing Speed • Safety & Acceptance 8

1. Challenge: building the NN 9

1. Challenge: building the NN • company policy: c++ 9

1. Challenge: building the NN • company policy: c++ • first tried caffe, stayed with it 9

1. Challenge: building the NN • company policy: c++ • first tried caffe, stayed with it • first try with VGG16 9

1. Challenge: building the NN • company policy: c++ • first tried caffe, stayed with it • first try with VGG16 • now VGG19 with custom layers for tracking (37 total) 9

1. Challenge: building the NN • company policy: c++ • first tried caffe, stayed with it • first try with VGG16 • now VGG19 with custom layers for tracking (37 total) • superior performance over previous algorithm 9

1. Challenge: building the NN • company policy: c++ • first tried caffe, stayed with it • first try with VGG16 • now VGG19 with custom layers for tracking (37 total) • superior performance over previous algorithm • problems: small objects 9

2. Challenge: Training 10

2. Challenge: Training • Broad vs. Random Training Initialization? 10

2. Challenge: Training • Broad vs. Random Training Initialization? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? • Same training set for all airports or specific? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? • Same training set for all airports or specific? • How many neurons, layers? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? • Same training set for all airports or specific? • How many neurons, layers? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? • Same training set for all airports or specific? • How many neurons, layers? • How many Epochs? 10

2. Challenge: Training • Broad vs. Random Training Initialization? • How many annotations needed per site? • Same training set for all airports or specific? • How many neurons, layers? • How many Epochs? 10

3. Challenge: inferencing speed 11

4. Challenge: Safety & Acceptance 12

4. Challenge: Safety & Acceptance • safety first in ATC 12

4. Challenge: Safety & Acceptance • safety first in ATC • need to prove performance 12

4. Challenge: Safety & Acceptance • safety first in ATC • need to prove performance • regulator decides if system may be used operationally 12

4. Challenge: Safety & Acceptance • safety first in ATC • need to prove performance • regulator decides if system may be used operationally • we treat ANN as human, same tests as for ATController 12

Example Images and Videos 13

Example Images and Videos • list a couple of sample sites and show actual video 14

Example Images and Videos 15

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Future Work • Optimal Flight Level Prediction • Optimal Aircraft to Gate Assignment • AI Controller Assist • many potential new application in ATC 19

Thank you for your time. HEAD OFFICE 
 19 Camelot Drive Ottawa, Ontario K2G 5W6 
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Challenge: Annotation 21

Plattform Screenshots