W4. Perception & Situation Awareness & Decision making

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1 W4. Perception & Situation Awareness & Decision making Robot Perception for Dynamic environments: Outline & DP-Grids concept Dynamic Probabilistic Grids Bayesian Occupancy Filter concept Dynamic Probabilistic Grids Implementation approaches Object level Perception functions (SLAM + DATMO) Detection and Tracking of Mobile Objects Problem & Approaches Detection and Tracking of Mobile Objects Model & Grid based approaches Embedded Bayesian Perception & Short-term collision risk (DP-Grid level) Situation Awareness Problem statement & Motion / Prediction Models Situation Awareness Collision Risk Assessment & Decision (Object level) Christian Laugier MOBILE ROBOTS AND AUTONOMOUS VEHICLES

2 Embedded Bayesian Perception Main features DP-Grids (see sessions 2 &3) Local navigable space & Conservative Collision Risk Perception process (reminder session 1) Sensor fusion +SLAM + DATMO (see sessions 5 & 6) Main difficulties Noisy data, Incompleteness, Dynamicity, Discrete measurements + Real time! Approach: Bayesian Perception Reasoning about Uncertainty & Time window (Past events & Near future) Improving robustness using Bayesian Sensor Fusion (Multiple sensors + Sensors & Dynamic models + Bayesian Inference) Scene Interpretation using Semantic & Contextual information 2

3 Embedded Bayesian Perception on a real vehicle DP-Grid based Architecture Bayesian Sensor Fusion + DATMO Fast Clustering & Tracking Algorithm (FCTA) Detected &Tracked Objects Architecture combining: DP-Grid construction Grid-based SLAM & DATMO BOF / HSBOF Construct & Update on-line (real-time) a DP-Grid model of the Dynamic Environment è Sensor Fusion & BOF / HSBOF Extract Object level data using FCTA & Specialized perception functions (e.g. lane tracking & localization) 3 [Mekhnacha 09] [Laugier et al ITSM 11]

4 Embedded Bayesian Perception on a real vehicle DP-Grid based Architecture Bayesian Sensor Fusion + DATMO Fast Clustering & Tracking Algorithm (FCTA) Laser Laser perception Detected &Tracked Objects BOF / HSBOF Laser Data Fusion (BOF / HSBOF) 8 layers, 2 lasers + Motion detection Odometry & IMU Filtered DP-Grid Laser impacts Sensor model : Independent beam model 4

5 Embedded Bayesian Perception on a real vehicle DP-Grid based Architecture Bayesian Sensor Fusion + DATMO Fast Clustering & Tracking Algorithm (FCTA) Stereo Vision Stereo Vision Detected &Tracked Objects BOF / HSBOF 5 Disparity data Obstacle U-Disparity Road U-Disparity Potential obstacle areas Road Obstacles Road (Navigable Space) U-Disparity OG Cartesian Occupancy Grid

6 Embedded Bayesian Perception on a real vehicle Stereo-vision perception (experimental results) 6 Scene view provided by vehicle front camera Superimposition of Road Free space (blue) & Obstacles (red) Display Cartesian Occupancy Grid & U-Disparity

7 Embedded Bayesian Perception on a real vehicle DP-Grid based Architecture Bayesian Sensor Fusion + DATMO Computer Vision Fast Clustering & Tracking Algorithm (FCTA) Computer Vision Specialized perception functions Detected &Tracked Objects BOF / HSBOF Detection & Classification (using Intensity & Depth Features) 7 Multi-Lane tracker & Localization (vision + Open Street Map)

8 Embedded Bayesian Perception platform Vehicle Architecture Lidars + Stereo + Inertial sensor + GPS Manycore Stereo camera 2 Lidars Current implementation PC + GPU + ROS (Manycore) GPU Nvidia Jetson New implementation (miniaturization) 8

9 Embedded Bayesian Perception platform System outputs Localization & Recorded Trajectory Open Street Map + GPS + IMU + Odometry Front view (camera) + Superimposition of detected moving objects Fusion result using BOF (DP-Grid) Navigable space & Conservative Collision risk OG from left Lidar OG from right Lidar OG from Stereo 9

10 An Embedded Bayesian Perception platform Experimental results (Bayesian sensor fusion, 25 Hz) left & right laser OG left & right laser raw data Resulting DP-Grid (occupancy + velocity) 10 Stereo vision OG

11 Estimating short-term collision risk DP-Grid level (using HSBOF) Objective Collision risk estimation at time t (horizon t+δ) Localization of risks in Space & Time Still before object analysis (DP-Grid level) δ = 0.5 s => Precrash δ = 1 s => Collision mitigation δ = 1.5 s => Warning / Braking Occupancy Grid Motion field Risk Camera view & Sensing step (δ s before the crash) Update frequency : 25 Hz 11

12 Estimating short-term collision risk DP-Grid level (using HSBOF) Approach Projecting over time the estimated scene (Particles & Occupancy) & Car model (Shape & Velocity) è Apply a conservative motion model (using current car motion data) Collision assessment for every next time step Integration of Risk over a time range [t t+δ] δ = 0.5 s => Precrash δ = 1 s => Collision mitigation δ = 1.5 s => Warning / Braking Dynamic cell Static obstacle Car model 12

13 Estimating short-term collision risk Experimental results Urban street Other Vehicle Mobile Puppet Ego Vehicle Crash scenario Results Detection a few seconds ahead of risky situations Urban è No false alarm (cars, pedestrians ) Crash scenarios è All collisions predicted before the crash (1s or 1.5s) 13

14 Estimating short-term collision risk Experimental results: Crash scenario Video Static Dynamic Risk 0.5s before crash Scenario 1 Crash scenario 1s before crash Scenario 2 14

15 Estimating short-term collision risk Experimental results: Crash scenario (video) Crash scenario 15

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