Towards Autonomous Vehicle. What is an autonomous vehicle? Vehicle driving on its own with zero mistakes How? Using sensors

Transcription

1 7 May 2017

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3 Disclaimer

4 Towards Autonomous Vehicle What is an autonomous vehicle? Vehicle driving on its own with zero mistakes How? Using sensors

5 Why Vision Sensors? Humans use both eyes as main sense for driving Images - huge amount of information : Recognition of objects/scenes TSR, TLR Geometry Surface type Passive Sensor

6 Main types of Vision Sensors Mono Vision Stereo Vision Obstacle detection based on image processing algorithms Signs recognition

7 Mono Vision

8 Mono Vision - Classification Pedestrian : Adult : Range 2m Pedestrian : Child : Range 2m Deep Algorithmic Learning Pattern Image Objects Acquisition Validation Detection - Pixels Decision Analysis

9 Mono Vision - Classification Types

10 False Positive (Ghost) System sees an obstacle that isn't really there

11 False Negative (Blindness) System fails to detect an obstacle Not Classified (Orientations / Missing classifier / Occlusions)

12 Non Classified Objects

13 Non Classified Objects

14 Non Classified Objects

15 Non Classified Objects

16 Non Classified Objects

17 Non Classified Objects

18 Non Classified Objects

19 Non Classified Objects

20 Non Classified Objects

21 Non Classified Objects

22 Non Classified Objects

23 Non Classified Objects

24 Non Classified Objects

25 Non Classified Objects

26 Non Classified Objects

27 Non Classified Objects

28 Stereo Vision

29 Stereo Vision Any Obstacle Closest obstacle : 1.1m Fareast obstacle : 2.9m Obstacle in the critical path : Range : 1.3 [M] Height : 97 [CM] Width : 39 [CM] Speed : 0 [KM/H] - Stationary Images Two Registration Synchronized Disparity Images Algorithmic and Pre-Processing Map Images Differences Decisions Creation Acquisition Calculation

30 Stereo Sensor Detect ANY type of obstacle without classification: Pedestrian of any size Animals Any Orientation / Shape Occlusions Aliens. Ideal for urban dense and crowded environment

31 Advantages of Stereo Vs. Mono Parameter Stereo-Vision Mono-Vision Obstacles Detection Unlimited Cars, Pedestrians, Cyclists Detection Accuracy High Medium Distance Accuracy High Medium Processing Time Low Medium to High SNR High Low Stains Filtration High Low Minimal Obstacle Size Very Small Medium Occlusions Sensitivity Not at all Medium to High

32 Complementary Technology for Detection Lidar, Radar, Ultra Sonic, Other Combination of Vision + additional technology Obstacle Dilemma reduced Active sensors reduction Best of breed of each technology

33 Stereo Trend on the rise Trend on the rise - Mercedes, Subaru, Land Rover, Jaguar Will be available for all vehicle categories From premium Compact vehicles Why wasn t this on the rise till now? Technology - Sophisticated Algorithms Processing power - Detection at first frame Perception of high Price (Stereo double than Mono)

34 Foresight Unique proposition Based upon16 years of know-how in Stereo vision All Weather conditions Aiming at 100% detection Aiming at 0% false alarms Detection by first frame Small object detection (40x40cm and less)

35 Foresight Stereo System 2 day cameras Detection at first frame Advanced algorithms

36 Foresight Stereo System 2 day cameras Detection at first frame Advanced algorithms

37 Stereo High Conformity with EuroNCAP EuroNCAP future requirements Product shall remain active in low visibility conditions Product shall detect cyclists Product shall detect adults and children Product shall detect pedestrians carrying objects that hide them partially

38 Summary Detection layer is a MANDATORY prerequisite for classification Detection MUST not rely on limited list of classifiers Stereo Vision provides the required (missing) layer Foresight Unique solutions are designated to provide: All Weather condition Near 100% detection & Near 0% false alarm Detection by first frame ALL Objects Detection Passive Sensor

39 Video From Another World

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