7 May 2017
Disclaimer
Towards Autonomous Vehicle What is an autonomous vehicle? Vehicle driving on its own with zero mistakes How? Using sensors
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
Main types of Vision Sensors Mono Vision Stereo Vision Obstacle detection based on image processing algorithms Signs recognition
Mono Vision
Mono Vision - Classification Pedestrian : Adult : Range 2m Pedestrian : Child : Range 2m Deep Algorithmic Learning Pattern Image Objects Acquisition Validation Detection - Pixels Decision Analysis
Mono Vision - Classification Types
False Positive (Ghost) System sees an obstacle that isn't really there
False Negative (Blindness) System fails to detect an obstacle Not Classified (Orientations / Missing classifier / Occlusions)
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Non Classified Objects
Stereo Vision
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
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
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
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
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)
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)
Foresight Stereo System 2 day cameras Detection at first frame Advanced algorithms
Foresight Stereo System 2 day cameras Detection at first frame Advanced algorithms
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
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
Video From Another World