Autonomous Integrity Monitoring of Navigation Maps on board Vehicles - PowerPoint PPT Presentation

Autonomous Integrity Monitoring of Navigation Maps on board Vehicles Philippe Bonnifait Professor at the Université de Technologie de Compiègne Heudiasyc UMR 7253 CNRS, France In collaboration with Clément Zinoune and Javier Ibanez-Guzman Renault S.A.S. PPNIV 2015, Hamburg, 28 September 2015 Ph. Bonnifait 1

Outline Context and Problem Statement Fault Detection Isolation and Adaptation Principles Adaptation to noisy data Conclusions and Perspectives Ph. Bonnifait 2

Turn-by-turn navigation system Navigation Map Driver interface for turn-by-turn guidance Destination Map-matching and Route Planning GPS Navigation function Ph. Bonnifait 3

Map-Aided ADAS Example: Intersection Warning Vehicle sensors Driver commands CAN bus Navigation Map Speed Yaw rate Odometer ... Map-matching and EH Electronic Horizon computation GPS Navigation function  Electronic Horizon (EH): representation of oncoming context events (e.g., curve, speed limits, intersection, etc.) Ph. Bonnifait 4

Map-Aided ADAS Example: Intersection Warning Engine control Brakes Vehicle sensors Cluster / HMI Driver commands CAN bus Distance to intersection Warning request Navigation Map Speed Current Speed Braking request Yaw rate Odometer ... Intersection warning Map-matching and EH Electronic Horizon computation GPS Navigation function  Electronic Horizon (EH): representation of oncoming context events (e.g., curve, speed limits, intersection, etc.) Ph. Bonnifait 5

Problem Statement  Map errors may be due to: o Errors during the mapping process. o Evolution of road network.  What happens if the map is wrong ? o Uncomfortable and unsafe situations. o Repetitive ADAS malfunctions. Ph. Bonnifait 6

Curve warning system Navigation map 30 m Ph. Bonnifait 7

Curve warning system GPS logs on top of the vehicle navigation map 30 m Ph. Bonnifait 8

Curve warning system Missed detection of the road bend. 30 m Ph. Bonnifait 9

Problem Statement 1. Evaluate navigation system integrity in real-time. 2. Provide a correction when necessary. 3. Use only on board vehicle sensors. Ph. Bonnifait 10

Outline Context and Problem Statement Fault Detection Isolation and Adaptation System architecture Methods for structural and geometrical faults Experimental results Adaptation to noisy data Conclusions and Perspectives Ph. Bonnifait 11

Autonomous integrity monitoring Navigation Navigation Map Function EH Map Client Systems GNSS Matching Ability of the vehicle to assess the confidence associated to navigation information using redundant information from on board sensors.  every trip on the same road adds redundancy To provide a reliable confidence indicator to avoid client systems malfunctions. Ph. Bonnifait 12

Architecture for Autonomous Integrity Monitoring Navigation Navigation Map Function EH Map Matching Client Systems GNSS 1 Correction Knowledge of fault Don’t use GNSS 2 Unknown Use Integrity of Navigation Information Proprioceptive sensors Smart front camera Memory Ph. Bonnifait 13

Definitions Fault : Error generative process. Error : Discrepancy between measured value and true value. Failure : Time when a function exceeds the acceptable value. A1 A2 A Ph. Bonnifait 14

Case of Navigation Fault : GNSS multipath; Wrong road candidate selected by map-matching ; Wrong representation of the road network. Error : Discrepancy between value in the EH and true value. Failure : Dysfunction of a client ADAS or autonomous driving function. Navigation Navigation Map Function EH Map Client Systems GNSS Matching Correction Knowledge of fault Don’t use GNSS Unknown Use Integrity of Navigation Proprioceptive Information sensors Smart front camera Ph. Bonnifait 15 Memory

Map Geometric Faults Ph. Bonnifait 16

Geometric Fault Detection, Isolation and Adaptation The vehicle position is encoded with: The curvilinear abscissa s The trip number k Ph. Bonnifait 17

Geometric Fault Detection, Isolation and Adaptation The vehicle position is encoded with: The curvilinear abscissa s The trip number k Ph. Bonnifait 18

Geometric Fault Detection, Isolation and Adaptation The vehicle position is encoded with: The curvilinear abscissa s The trip number k FDIA is based on the comparison of vehicle position estimates: G from vehicle sensors N from the Navigation function estimate Ph. Bonnifait 19

Geometric fault detection, isolation and adaptation Detection : Determine whether an estimate is affected by a fault Isolation : Determine which estimate is affected by a fault Adaptation : Identify a fault free estimate to provide it to client systems Ph. Bonnifait 20

Assumptions When travelling several times on a road, the vehicle follows the same path with small deviations At a given abscissa: o Faulty vehicle position estimates from sensors are different from one trip to the other. o Faulty vehicle position estimates from the navigation are always the same. o Faults on the vehicle position estimates from sensors and from the navigation are different from each other. Ph. Bonnifait 21

Method  First vehicle trip  Two independent estimates of the vehicle position: ◦ G 1 (from vehicle sensors) ◦ N 1 (from navigation system)  Observed residual:  G 1 affected by a fault:  N 1 affected by a fault: Ph. Bonnifait 22

Faults and residuals Possible outcomes ◦ G 1 ≠ N 1 G 1 = N 1  One estimate is faulty Both estimates are fault-free and and and  Both estimates are faulty and Faults on estimates from sensors and from the navigation are assumed to be different from each other The residual is therefore the result of a Boolean OR: Ph. Bonnifait 23

Method Compute the residual based on the available estimates Find this residual in the truth table Provide the knowledge of fault to client systems Ph. Bonnifait 24

Illustrative example: First trip s = 10m G 1 ≠ N 1 Abscissa s (m) Both estimates are possibly faulty A fault is detected but not isolated The method returns Unknown Abscissa s (m) Ph. Bonnifait 25

Illustrative example: First trip s = 20m G 1 = N 1 Abscissa s (m) There is no fault The method returns Use Abscissa s (m) Ph. Bonnifait 26

Illustrative example: First trip s = 40m G 1 ≠ N 1 Abscissa s (m) Both estimates are possibly faulty A fault is detected but not isolated The method returns Unknown Abscissa s (m) Ph. Bonnifait 27

Using several trips  Second vehicle trip  Two new estimates of the vehicle position at the same abscissa ◦ G 2 (from vehicle sensors) ◦ N 2 (from navigation system) N 1 G 1 N 2 G 2  Observed residual vector: Ph. Bonnifait 28

Faults and residuals Possible outcomes when comparing two estimates from sensors G 1 and G 2 G 1 = G 2 ◦ G 1 ≠ G 2 Both estimates are fault-free  One estimate is faulty and and and  Both estimates are faulty and Errors on estimates from sensors are assumed to be different from one trip to the other The residual is therefore the result of a Boolean OR: Ph. Bonnifait 29

Faults and residuals Possible outcomes when comparing two estimates from Navigation N 1 and N 2 N 1 = N 2 : Map faults ◦ N 1 ≠ N 2 Matching faults Both estimates are fault-free  One estimate is faulty and and and Both estimates are faulty and Errors on the vehicle position estimates from the navigation are always the same The residual is therefore the result of a Boolean Exclusive OR: Ph. Bonnifait 30

Truth table for two trips Ph. Bonnifait 31

Illustrative example: Second trip s = 10m s (m) This residual is unique in the table, isolation is done The current navigation is found not faulty s (m) The output Use is provided to client systems Ph. Bonnifait 32

Illustrative example: Second trip s = 20m s (m) This residual is unique in the table, isolation is done The current navigation is found not faulty s (m) The output Use is provided to client systems Ph. Bonnifait 33

Illustrative example: Second trip s = 40m s (m) This residual is four times in the table, fault is s (m) detected but not isolated The output Unknown is provided to client systems Ph. Bonnifait 34

Formalism properties  Guaranteed detection of faults Ph. Bonnifait 35

Formalism properties  Guaranteed detection of faults  Conservation of residual isolability Ph. Bonnifait 36

Formalism properties  Guaranteed detection of faults  Conservation of residual isolability  Isolation convergence Ratio of adverse residuals One trip: q(1) = 3/4 • Two trips: q(2) = 6/16=3/8 • Infinity: q(Inf)  0 • Ph. Bonnifait 37

Formalism properties  Guaranteed detection of faults  Conservation of residual isolability  Isolation convergence Ratio of adverse residuals One trip: q(1) = 3/4 • Two trips: q(2) = 3/8 • Infinity: q(Inf)  0 •  Adaptation There is at least one non faulty estimate in isolable sets of faults Ph. Bonnifait 38