Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on 16.422 Information & Signal Detection Theory Prof. R. John Hansman Acknowledgements to Profs Tom Sheridan and Jim Kuchar whose notes are the core of this lecture Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Outline y Information Theory y Signal Detection Theor y y Alerting Introduction Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Information Theory y What is information? y Control Theoretic Vie w ? Lines in Control Block Diagram y Ba yesian Vie w ? Information is something which reduces uncertaint y in a world model System Supervisory Control Computer Interface DisplayControl Sensors Direct Observation Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Bayes Theory H = Hypothesis, D = Datap(H | D) = p(D | H) p(H)/p(D) With new datap(H | D1,D2) = p(D2 | H) [p(D1 | H) p(H)/p(D1) /p(D2) With 2 hypothesesp(H1 | D1,D2) = p(D2 | H1) p(D1 | H1) p(H1) p(H 2 | D1,D2) p(D2 | H2) p(D1 | H2) p(H2) Posterier odds ratio prior odds ratio Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on y Shannon Information Theor y ? Bell Labs ? Telephon y y Tom Sheridan Notes (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on y Bit view of Information ? # of bits to disambiguate ? Bit = binar y discrimination ? Drive uncertaint y to zero (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Info Transmission H = information, D = Data (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Information Value (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Information Bandwidth y Information Rate ? Bits/Sec y Information Densit y y Raster Example ? (# Pixels) ( # Bits/Pixel) (Update Rate) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Task Performance & Bandwidth Frame Rate Constant Task Performance ColorDepth Constant Bitrate Resolution Diagram from Sheridan, Teleoperation Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Signal Detection Theory y Originall y De veloped for Radar Threshold Detection y Becomes the Basis for Alerting Theor y y Signal v e rsus N oise y A-Scope Example Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on (Courtesy of Thomas Sheridan. Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Consider Sensor System System Threshold Display Or Alert Sensor y Radar y Engine Fire Detection y Other Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Threshold Placement 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 .0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 0 Probability of False Alarm P(FA) Probability of Successful Alert P(SA) Example Alerting Threshold Locations Ideal Alerting System 1 2 ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Engine Fire Alerting 207.7 +10c LBS X 1000 TOTAL FUELTEMP FIRE ENG R 1,250 1,380 CRZ 723394 1,250 1,380 723394 EPR N 1 EGT TAT +15c y C(F A ) high on takeoff y A l erts suppressed during TO No w let’s take a quick look at non-normal checklists. T he 777 EICAS message list is similar to other Boeing EICAS airplanes. [ For 747-400 o perators : It doesn’t use the “caret” s y mbol to indicate a chec klist w i t h no QRH items, like the 747- 400s do.] But it has an additio nal featur e, called t he “c hecklist ico n”. T he icon is displayed ne xt to an EICAS message w h enev er there i s an EC L checklist that needs to be c o mpleted. Once the chec klist is full y c o mplete, the icon is removed fr om displ ay next to the message. T h is helps the cre w k eep t r ack of w h ic h c hecklists remai n to be completed. W 015. 23 Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Crew Alerting Levels Non-Normal Procedures Time Critical Operational conditi on t h at requires immediate crew aw areness and immediate action Warning Operational or s y s t e m condition that requires immediate crew aw areness and definite corrective or compen sator y actio n Caution Operational or s y s t e m condition that requires immediate crew aw areness and possible corrective or compen sator y actio n Adv i s o r y Operational or s y s t e m condition that requires crew aw areness and possible corrective or compensatory action A l ternate Nor mal Procedures Comm A l erts crew to i n co mi ng datalink communication Memo Crew remin ders of the current state of certain manually selected n ormal co nd iti o n s Source: Brian Kelly Boein g Don’t hav e time to discuss these levels. Important thing to kno w is that w e ri goro usl y define a nd def end these l e vel s We appl y them across all the s y stems. T he indications are consistent for all alerts at each lev el. T hus the pilots instantl y kno w the criticalit y a nd nature of an alert even bef ore the y k n o w w h at the prob l e m is Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Boeing Color Use Guides Red Warnings, warning level limitations Amber Cautions, caution level limitations White C urrent status information Green Pilot selected data, mode annunciations Magenta Target information Cyan Background data Again, w e don’ t have time to describe these definiti ons in detail. T he important thing to note is t hat our phi losophy is definite, and as simple as practical. It fits on one p a g e , in bi g font no less. Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Access To Non-Normal Checklists y Prevents choosing w r ong checklist FIR E E N G R FIRE ENG R When an al ert message is d i s p la ye d, the pilo t simpl y p u she s the CHKL but ton and the cor rect non-nor m a l checklist is di spla ye d. T his prevents the cr e w from acc identall y choosi ng the w r ong check list. T he non-norm al checkl ists have p riorit y over the normal ch ecklists. Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Non-Normal Checklists W 015. 27 NORM AL ITEM OVRD NOTES CHKL OVRD CHKL RESET Fire is detected in the right engi ne. RIGHT AUTO THROTTLE ARM SWITCH . . . . . . . . . . . OFF RIGHT THRUST LEVER . . . . . . . . . . . . . . . . . . . . . .CLOSERIGHT FUEL CONTROL SWITCH . . . . . . . . . . . . . C UTOFF RIGHT ENGINE FI RE SWITCH . . . . . . . . . . . . . . . . . . P ULL If FIRE ENG R m e ssage remains displayed: NORMAL MENU RESETS NON-NORMAL MENU 9 999 FIRE ENG R RIGHT ENGINE FIRE SWITCH . . . . . . . . . . . . . ROT ATE Rotate to the stop a n d ho ld fo r 1 secon d. 9 3 2 1 y Checklist specific to left or right side y Exact s w itch specified y Memory items already complete y Closed-loop conditional item y Page bar T h is is w hat a typical normal c hecklist looks like. T h is is the Preflight check i s t. T here are t w o kinds of line items, w hic h w e c a ll open-l oop and closed-loop items. T he open-loop items have a gray check-box in front of them. T hese are items that the airpl ane s y stems cannot sense. T he pilot determines w h et her th e i tems h a ve bee n com pleted a nd clic ks the CCD thu mbs w itch w h e n ea ch item is complete. Close d-lo op ite ms are for s w it ches and se lec tors that are sensed b y the air plan e s y stems. T h e y automat icall y turn gr ee n w h en th e s w i tch has been positioned correctl y . If the crew actuates the w r ong s w itch, the closed-loop item w i ll no t turn green and the crew w i ll catch their error. In this exa mpl e, the procedur e w as already c o mpl ete, so the last t w o items are sho w n in green as soon as the che cklist is displ ayed. T h e w hite curr ent line item b o x le ads the pi lo t through the checklist an d pr ev ents accid en tall y sk ipp ing a line item. Color is us ed to indic ate lin e i t em status. Incomplete items ar e disp la ye d w h ite an d com plete items are displa ye d gre e n. C y an ( or b l ue) indicates an inapplic abl e i t em, or an item that has been intentional l y overridden by the crew u s ing the ITEM OVRD button. In this example, the flight is dispa tc h i ng w i t h autobr ake s inoper ative, so the cre w has overr i dden the AUT OBRAKE item. Overriding the item allo w s t he checkl ist to be compl eted. Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Internal vs External Threat Systems y Internal ? S ystem normall y well defined ? Logic relatively static ? Simple ROC approach valid ? Examples (Oil Pressure, Fire, Fuel, ...) y External ? External environment ma y not be well defined ? Stochastic elements ? Controlled s y stem trajector y m a y be important ? Human response ? Need ROC like approach which considers entire s y s t em ? S ystem Operating Characteristic (SOC) approach of Kuchar ? Examples (Traffic, Terrain, Weather, …) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Decision-Aiding / Alerting System Architecture Sensors Displays Human Actuator Sensors Automation Actuator Environment P r o c e s s Information Transduction Decision Making Control / Actuation Interface ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Fundamental Tradeoff in Alerting Decisions y When to alert? ? Too earl y o Unnecessar y Alert ? Operator would have avoided hazard without alert ? Leads to distrust of s y stem, dela y ed response ? Too late o Missed Detection ? Incident occurs even with the alerting s y stem y Must balance Unnecessary Al erts and Missed Detections Hazard Uncertain Future Trajectory Uncertain current state x 1 x 2 ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on The Alerting Decision y Examine consequences of alerting / not alerting ? Alert is not issued: Nominal Trajector y (N) ? Alert is issued: Avoidance Trajector y (A) A Hazard A Current State Hazard N Compute probabilit y of Incident along each trajector y ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on System Operating Characteristic Curve 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 .0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 0 Probability of False Alarm P(FA) Probability of Successful Alert P(SA) Example Alerting Threshold Locations Ideal Alerting System 1 2 ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Trajectory Modeling Methods Nominal Worst-case Probabilistic ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Nominal Trajectory Prediction- Based Alerting y Alert w h e n projected trajectory encounters hazard y Look ahead time and trajector y model are design parameters y Examples: TCAS, GPWS, AILS hazard system state predicted nominal trajectory ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Airborne Information for Lateral Spacing (AILS) (nominal trajectory prediction-based) Endangered aircraft vectored away Alert occurs with prediction of near miss in given time interval ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Alert Trajectory Prediction- Based Alerting y Alert is issued as soon as safe escape path is threatened y A t tempt to ensure minimum lev el of safety y Some loss of control ov er false alarms y Example: Probabilistic parallel approach logic (Carpe nter & Kuchar) hazard system state predicted escape path (alert trajectory) n o m i n a l t r a j e c t o r y ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Example State Uncertainty Propagation Computed via Monte Carlo -50 0 50 0 50 100 150 Nautical Miles Nautical Miles t = 2 min t = 5 min t = 10 min t = 15 min t = 20 min along-track V = 15 kt cross-track V = 1 nmi (from NASA Ames) ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Monte Carlo Simulation Structure Monte Carlo Simulation Engine Protected Zone size Uncertainties (probability density functions) Current states Along- and cross-track error Maneuvering characteristics Confidence in intent information Current state information (position, velocity) Intent information: Waypoints (2D, 3D, 4D) Target heading Target speed Target altitude Target altitude rate Maneuvering limitations Probability of conflict Implemented in real-time simulation studies at NASA AmesComputational time on the order of 1 sec ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on System Operating Characteristic Curve 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 .0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. 0 Probability of False Alarm P(FA) Probability of Successful Alert P(SA) Example Alerting Threshold Locations Ideal Alerting System 1 2 ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Aircraft Collision Avoidance human ai rc ra ft sen s ors ex peri ence, t r ai ni n g ot he r i n f o . (e.g. w i ndo w v i e w ) hu m an s e nses diagnosis and control con t rol s GPW S alert and decis ion aid c a ut i o n: " t e r r a i n " automation w a rni n g: " p ul l up" d i sp la ys a l t i tude a nd al ti t u de ra t e ot h e r sensor i n form ati on t e rr ai n dat a Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Fatal Accident Causes Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erf ac e Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Prototype MIT Terrain Alerting Displays Enhanced GPWS Impro v es Terrain/Situational Aw a r eness + 2,000-ft high density (50% ) red + 1,000-ft high density (50% ) y ellow Referen ce altitud e - 250/-500-ft medium density (25 % ) y e llow - 1 ,000-ft medium densit y (25 % ) green - 2 ,000-ft medium densit y (12.5 % ) green EFIS map display color legend W002W.43 Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Terrain Alerting TAWS Look-Ahead Alerts (Terrain Database) “Caution Terrain” approx 45 sec “Terrain, Terrain, Pull Up...” approx 22 sec. Basic GPWS modes (radar altitude) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on TAWS Look-ahead Warning y Threat terrain is sho w n in solid red y “Pull up” light or PFD message y Colored terrain on na v i gation displa y Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Conflict Detection and Resolution Framework Environment Dynamic Model Conflict Detection Conflict Resolution Current States Projected States Metrics Human Operator State Estimation Intent Metric Definition ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Multiple Alerting System Conflicts ? D ev eloping formal methods for system analysis ? Identification of conflicts and methods to mitigate? Driv ers / implications for human interaction ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Design Principles for Alerting and Decision-Aiding Systems for Automobiles James K. Kuchar Departme nt of A e ronautics and A s tronautics Massachu setts Institute of Tech n o l o gy ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Kinematics y A l ert time: t alert = (r - d)/ v t alert = 0 J braking must begin immediately t alert = W J alert is issued W seconds before braking is required y Determine P(UA ) and P(S A) a s function of t alert Vehicle Hazard r Alert Issue d d Total Braking Distance Response Latency Braking Distance v W ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Example Human Response Time Distribution 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 012 34 5 Time (s) Lognormal distribution (mode = 1.07 s, dispersion = 0.49) [Najm et al.] ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Case 3: Add Response Delay Uncertainty 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 P(UA) P(SA) ? 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 t alert ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Case 4: Add Deceleration Uncertainty 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 P(UA) P(SA) 4. 0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 ? 0.0 t alert V a = 3 ft/s 2 ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Conformance Monitoring for Internal and Collision Alerting y Simple Sensor Based Collision Alerting S ystems Do Not Pro v ide Adequate Alert Performance due to Kinematics ? SOC Curve Anal y s is ? P(FA), P(MD) Performance y Enhanced Collision Alerting Sy s t ems Require Inference or Measurement of Higher Order Intent States ? Automatic Dependent Surveillance (Broadcast) ? Environment Inferencing ? Observed States ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on SURVEILLANCE STATE VECTOR y Aircraft Sur v eillance State Vector, X ( t ) containing unce rtainty & errors G X ( t ) is given b y: ? Traditional d y namic states ? Intent and goal states  X(t) P osition, R(t) Velocity, V(t) Acceleration, A(t) Intent, I(t) Goals, G(t)  -  ? °°°°ˉ °°°° ?  ? °°°°? °°°° ,  G X ( t ) G R (t) G V (t) G A (t) G I (t) G G(t)  -  ? °°°°ˉ °°°° ?  ? °°  °°? °°  °° ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on INTENT STATE VECTOR y Intent State Vector can be separated into current target states and subsequent states I ( t ) Current tar g et states Subsequent planned trajectory -  ?ˉ ?  ?? (Eqn. 3) ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Automobile Lateral Tracking Loop Vehicle Goal Selection Route Selection Lane/ Li ne Selection Lane/ Li ne Tracki ng External Environment - Default - O p e n Loop - O ptimized - C ommande d - P rior Hi stor y -I n s t r u c t e d -W a n d e r Route DesiredLi ne SteeringComm and Ve h ic le States -B e s t L i n e - L a n e Sw itching - T r a ffi c -S p e e d Goal Whee l Posit io n (forc e) A c c eleratio n Velocity Positi on Ha za rd Monitoring Threats D i s t u r ba nce s Strategi c Fa cto r s X = (Goal, Subsequent Planned Trajectory , Cur rent Ta rget State, A c celeration, Velocity , Position) ( Courtesy of J a m es Kuchar . Used with permission.) Sy ste m Su pe rvi so ry Co ntr ol Co mp ute r Int erfa ce Di sp lay Co ntr ol Se nsor s Di rec t O bs erv ati on Intent Observability States y Road w ay y Indicator Lights ? Break Lights ? Turn Signals ? Stop Lights y A c c e leration States y GPS Routing y Head Position y Dynamic History y Tracking Beha vior ( Courtesy of J a m es Kuchar . Used with permission.)