The Coming Transition in Automobile Cockpits - Insights from Aerospace Prof. R. John Hansman Department of Aeronautics & Astronautics Evolution of Cockpit Displays Software Growth in Aircraft B757/767-200 B777-200 B747-400 B747-200 0 25000 50000 75000 100000 125000 150000 1970 1975 1980 1985 1990 1995 2000 Year Total Airplane Signals, Digital, Analog Words Empirical Data Extrapolation FBW Correction ? Software size doubles every 18 months ? Compensating for “FBW offset” reduces doubling to 33 months Hypothesis ? We are entering a period of significant change in automobile Human-Machine Interaction driven by Information Technologies ? Automobiles will undergo a change more substantial than the change in aircraft from “steam gauge” to “glass cockpits” Car / Aircraft Comparison ? Market Capital investment (ROI) Consumer product ? Number of vehicles (US) 300,000 200,000,000 ? Safety (US) 663 fatalities (1998) 41,000 fatalities (1997) ? Threat response time constant Order 5-60 sec. Order 1 sec. ? Hazard density Low, 3-D collision (vehicle, terrain, animal), WX High, 2-D collision (vehicle, object, person, animal, …) ? System complexity High Med/low Car / Aircraft Comparison (cont.) ? Operator selectivity/training/medical High Low ? Tracking precision (Heading) Order 5° Order 1° ? Recurrent training Yes No ? Operations procedure Yes No ? Impaired operators (Alcohol, Drugs) Order 1/10 7 -10 9 Order 1/10 4 -10 5 Aerospace Systems Applicable to Cars ? Control systems – ABS – Stability augmentation ? Fly by Wire/Light (FBW,FBL) – Integrity Concerns (eg 777) ? Critical software systems ? Fault tolerant systems ? Head up displays (HUD) ? Helmet mounted displays (HMD) ? Synthetic Vision Systems ? Sensor Fusion ? Hands on throttle and stick (HOTAS) ? Dark cockpit ? Navigation systems – GPS, DGPS – IRS/GPS ? Situation awareness displays – Moving map – Database ? Caution and Warning Systems ? Collision Alerting Systems ? Tactile alerting – Stick shaker ? Master caution – Information accessibility – Maintenance Diagnostics Example:Phase Carrier Differential GPS in Automobiles ? High Precision (5 cm) – Demonstrated in UAV Applications ? Slip Angle Measurement – Dual Antenna ? Performance Evaluation ? Preliminary Testing Issues – High Dynamic Environment – High “Jerk” States With Prof. Jon How Dept of Aeronautics & Astronautics Track Hardware Layout ? Two 2 GPS antennas were mounted on the car to form a single baseline ? Data-Linc Group (SRM6000) Modem antenna also attached to roll bar – Real-time communication with ground station MIT Run Results Typical Performance Relative position error ? Precise state determination – 2 - 5 cm position error – 1 - 2 cm/s velocity error – 1 - 2 degrees heading – @ 5 - 10 Hz Track Results - Slip Measurements ?Carheading and velocity vectors not aligned Track Results -Slip Measurements Comparative Lap Results Attachment converted: John's G3 Laptop:vel_comp_jph_rjh.jpg (JPEG/JVWR) (00010F90) Acceleration vs Position “Human Centered” Information Requirements Analysis ? Integrated Human Centered Systems Approach ? “Semi-Structured” Decision Theory ? Driver Distraction Analysis Driving Task Definition Vehicle Secondary Systems (eg: cell phone) Vehicle Operation Task (Primary Task) Everything Else (Secondary Tasks) Driver Distraction Potential Secondary Systems (eg: cell phone) Everything Else (Secondary Tasks)r n Primary Task Analysis Driver Vehicle Secondary Systems (eg: cell phone) Vehicle Operation Task (Primary Task) Everything Else (Secondary Tasks) Distraction Potential Vehicle Operation Tasks ? Vehicle control tasks [skill based] – Lateral control (steering) – Longitudinal control (accel., braking) ? Tactical decisions [rule based] – Maneuvering – Systems management ? Strategic decisions [knowledge based] – Route selection – Goal management ? Monitoring [skill, rule, knowledge] – Situation awareness [Rasmussen: Skill Rule Knowledge Hierarchy] Lateral Tracking Loop Vehicle Goal Selection Route Selection Lane/Line Selection Lane/Line Tracking External Environment Steering Command - Default - Open Loop - Optimized - Commanded - Prior History - Instructed -Wander -Best Line - Lane Switching - Traffic -Speed Route Desired Line Vehicle StatesGoal Wheel Position (force) Hazard Monitoring Threats Disturbances Strategic Factors Acceleration Velocity Position Driver Input/Output Modes Driver Visual - External Scene - Forward - Roadway - Traffic - Lights - Signage - Environment -Peripheral -Rear - Internal - Instrument Cluster Audio -Roadway - Alerts - Horns (ext) - Chimes/Clicks - Velocity (Wind) - Engine -Radio - Passenger Manual (Hand) -Wheel - Steering Column - Gear Shift - Switch (Panel) - Switch (Other) Tactile/Proprioceptive - Lateral Accel - Longitudinal Accel - Road Surface - Control Force Feedback -Vibration - Switch Feedback Olfactory - Gasoline -Smoke/Fire - Passenger Manual (Foot) - Throttle -Brake -Other Voice Other - Eye Tracking - Blink -Gesture - Thought Other “Situation Awareness” ? Term originally defined for air combat ? Working Definition: Sufficiently detailed mental picture of the vehicle and environment (i.e. world model) to allow the operator to make well-informed (i.e., conditionally correct) decisions. Driver Situation Awareness Components Situation Awareness Weather Passengers Control Settings Equipment Status Personal Factors Vehicle Performance Location/ Route Adjacent Environment Roadway Surface Adjacent Traffic Signage Non-driving Elements Internal External Endsley Situation Awareness Model ?System Capability ?Interface Design ?Stress & Workload Performance Of Actions Decision Perception Of Elements In Current Situation Level 1 Comprehension Of Current Situation Level 2 Projection Of Future Status Level 3 ?Goals & Objectives ?Preconceptions (Expectations) Information Processing Mechanisms Long Term Memory Stores Automatically ?Abilities ?Experience ?Training State of the Environment Feedback Task/System Factors Situation Awareness ?Complexity ?Automation Individual Factors Driving Task Definition Driver Vehicle Secondary Systems (eg: cell phone) Vehicle Operation Task (Primary Task) Everything Else (Secondary Tasks) Distraction Potential Interaction Metaphors ? Car as image statement ? Car as clothing ? Car as jewelry ? Car as sports equipment ? Car as safe space ? Car as cocoon ? Car as home ? Car as kitchen ? Car as bathroom ? Car as bedroom ? Car as music room ? Car as playroom ? Car as entertainment ctr ? Car as toolbox ? Car as closet ? Car as office ? Car as comm center Trends in Driver Attentional Demand D r i v e r D r i v e r A t t e n t i o n a l A t t e n t i o n a l D e ma n d D e ma n d Vehicle Operation Secondary Tasks R a d i o R a d i o T a p e T a p e C e l l P h o n e C e l l P h o n e T r i p C o m p u t e r T r i p C o m p u t e r V i d e o V i d e o W e b W e b Time Time Spent in Vehicles (US) - Average 350 hrs/yr/person - 500 Million hrs/week 76% of Drivers Report Activities Have “Caused/Nearly Caused” an Accident 0% 5% 10% 15% 20% 25% 30% SPILLING COFFEE BREAKING UP FIGHT BETWEEN KIDS CIGARETTE ASHES USING COMPUTER TURNING TO SPEAK TALKING ON CELL PHONE Source: Opinion Research Corp Interviews, Time5/8/00 (N=1016) Concerns Regarding High Secondary Task Loads ? Growing Evidence and Public Perception of Safety Problem ? Cell Phone use in US – 115-120 Million Active Cell Phones – 50-70% Use in Vehicles – 3.9% of Drivers Using (Daylight Hours) ? NE Journal of Medicine Estimate – 4 fold increase in collision risk using cell phone ? NHTSA Estimates – 1.2 Million Accidents (25-30%) caused by Distracted Driver ? Not limited to cell phones ? Note: These effects may be latent in normal operations and may only manifest in non-normal or emergency situations Typical Performance vs. Task Load Curve Performance Task Load Distraction Components ? Manual – Inability or delay in operation of vehicle control – Hands occupied or out of position (cell phone) ? Visual – Head Down Problems – Visual Accommodation – Visual Clutter – Visual Compulsion ? Cognitive – Lack of cognitive engagement with primary Task – Latency in mental context shifts – Multi-tasking capabilities – Prioritization ? Emotional ? High Individual Variability in Multi-Tasking Capability Distraction Data Sources ? Controlled Experiments – Vary Secondary Task Load (Independent Variable) ?Visual ? Cognitive – Measure ? Performance ? Response to Disturbance ? Situation Awareness – Need to Increase Task Loading to Saturation – Need to Include Unanticipated Events – Need Lowest Common Denominator Population ? Field Data – Event Recorders – Cell Phone Triggered ? Subjective Survey Data Controlled Experiment Issues (1) ? Simulator Testing – Controlled Scenarios + – Safe to go to Task Saturation – Face and Cue Validity Issues - – Simulator Sickness - –Cost - ? Dual Control Vehicle Testing (Test Track) – Good Validity + – Safety Issues at Task Saturation - –Cost + Controlled Experiment Issues (2) ? Variability in Primary and Secondary Tasking – How do you measure Secondary Task Load? – How do you control Secondary Task Load? ? Performance Measures – Tracking – Reaction Time – Side Task Performance ? Subjective Workload Measures ? Situation Awareness Measures – Testable Response Method Simulator Studies of Driver Cognitive Distraction Caused by Cell Phone Use MIT Age Lab Simulator ? Independent Variables – Cognitive Loading – Hands free/Hands Fixed ? Dependant Variables – Situation Awareness – Reaction Time – Tracking Cognitive Loading Levels ? Low – Small talk ? Medium – Discuss movie/opera/ballet plot-line ? High – Edit document by phone Results: Situational Awareness 0 0.2 0.4 0.6 0.8 1 Normal HF HH Phone Setup A ver ag e F r act i o n C o r r ect . 85% 66% 58% ? Significant reduction in SA with Cell Phone Use ? No significant difference between HF& HH Stop Sign Response Time Results by Age Group ? Significant effect of Age ? No significant effect of Cell Phone Use 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 Normal 1 Normal 2 HF Small HF Movie HF Edit HH Small HH Movie HH Edit Phone Set-up & Cognitive Level R esp o n se T i m e [sec] . Young Intermediate Older Overall Approaches to Enhancing Focus in the Information Rich Cockpit ? Heads-Up Operation – Parsing operating logic – Glance Display Designs (< 1 sec) – Tactile input devices ? Voice Input-Output – not a Panacea ? Prioritization Systems – Intelligent Situation Assessment – Interruption “Stand-by” Architecture Example ? Communications ? Information Systems ? Non Critical Warnings 300-MIT Testbed ? Collaboration – MIT Media Lab CC++ – Motorola – DiamlerChrysler ? Highly Instrumented Platform – External Environment – Internal Environment – Vehicle States – Driver Cognitive and Emotional States ? Prototype Platform ? Platform - Chrysler 300M “Standby” System Issue: Criteria for automatic “Standby Status 300M Experiment to Determine Indicators of Driver “Busy” States ? 20+ Subjects Drove Challenging Trajectory in Boston-Cambridge Area in 300M Instrumented Vehicle ? Subjects indicated transitions between “Busy” and “non-Busy” States ? Attempted Correlation with Observable States Example “Busy” State Data Example of indicated busy points Good Correlation with Specific Locations Merge onto Major Roadway Complicated Intersection With Merging Traffic Rotary Parking Lot with Pedestrians Narrow Side Street Complex Urban Location “Harvard Square” Weak Correlation with Simple Dynamic States Results Consistent With Subjective Reporting of High Workload Tasks ? Merge points ? Pedestrians ? Rotaries ? Narrow Streets ? Busy Intersections ? Unfamiliar Locations – Searching for Locations ? Construction Zones ? Poor Weather Conditions ? Potential for Adaptive Learning Algorithims with Multi-Attribute Correlations Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage Enhanced Vision Synthetic Vision Enhanced Vision Synthetic Vision ? Goal is to increase safety and capacity ? Challenge is to ensure no adverse effects are created Boeing is investigating these technologies, including evaluating prototype systems on the 737 Technology Demonstrator in early 2002. While these technologies hold promise for increasing safety and potentially improving airport capacity, the designs must be approached carefully to ensure no harmful side effects are induced. Enhanced Vision Picture of the outside world created by real-time weather and darkness penetrating on-board sensors (eg. Cameras, FLIR, MMW radar, and weather radar). Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays –GPS – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage GPS Progressive Route Guidance ? Progressive Turn Guidance ? Current Limitations – Complex Intersections – Database Resolution – Database Structure – C/A Code Precision – Limited command set ? Potential to degrade SA – Dependency – Not robust to interruptions/errors – Head Down – Lack of “Naturalistic Interface” ? Kamla Topsey & Kate Zimmerman Expt. Naturalistic Direction Study ? 13 Subjects: Directions categorized ? Most common types: – Street names and route signs 26% – Left/right turn indication 23% – Distance by Reference point ? Stoplights/ Stop signs 21% ? Landmarks 11% ? Least common types: – Distance by measurement 4% – Heading 1% K2 Navigation System Prototype ? No visual demand: voice-based ? Syntax: – Reference + Action + Target ? Examples: – “At the next light, make a left onto the street between UNO’s Pizzeria and Fleet Bank” – “Just after the Star Market bear right onto Belmont St.” Testing ? Systems: Map, Carin, K2 ? Routes: – Start from MIT – Use 15 commands – All include a rotary ? Subjects: – 2 males, 4 females – 20-21 years old – 4-5 years driving experience – Unfamiliar with driving in Boston area Results: Navigation Errors 0 2 4 6 8 10 12 Map Carin K2 An error is defined as any deviation from the intended path. Results: Comparative Ratings K2 vs. Carin K2 Carin K2 vs. Map Map K2 Carin vs. Map Carin Map Indifferent Recently Developed Weather Datalink Products ARNAV Vigyan Bendix/King FAA FISDL Avidyne Echo Flight Garmin Control Vision UPS – AirCell Digital Cyclone Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage Fundamental Tradeoff in Alerting Decisions ? When to alert? – Too early oUnnecessary Alert ? Operator would have avoided hazard without alert ? Leads to distrust of system, delayed response – Too late oMissed Detection ? Incident occurs even with the alerting system ? Must balance Unnecessary Alerts and Missed Detections Hazard Uncertain Future Trajectory Uncertain current state x 1 x 2 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 Unnecessary Alert P(FA) Probability of Successful Alert P(SA) Example Alerting Threshold Locations Ideal Alerting System Kinematics Vehicle Hazard r Alert Issued d Total Braking Distance Response Latency Braking Distance v W ? Alert time: t alert = (r - d)/v t alert = 0 J braking must begin immediately t alert = WJ alert is issued W seconds before braking is required ? Determine P(UA) and P(SA) as function of t alert ? V = 35 mph in following example Case 1: Perfect Sensors 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 P(UA) P(SA) > 2.51.0 - 2.5 < 1.0 t alert W = 1.0 s, a = 1/2 g Deterministic: probabilities are 0 or 1 Ideal performance achievable for t alert between 1.0 - 2.5 s Case 2: Add Sensor 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) 3.0 t alert 2.5 1.45 - 1.75 1.0 ? 0.5 V r = 0.7 m V v = 0.4 m/s W = 1.0 s a = 1/2 g Nearly ideal performance for t alert between 1.45 - 1.75 s Example Response Time Distribution 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 012345 Time (s) Lognormal distribution (mode = 1.07 s, dispersion = 0.49) [Najm et al.] 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 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 Kinematics Sensors (eg RADAR) Limited by Vehicle Dynamics and Response Time Need Intent States 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 Courtesy of Prof Jim Kuchar, MIT Dept of Aero/Astro Intent States in the Lateral Tracking Loop Vehicle Goal Selection Route Selection Lane/Line Selection Lane/Line Tracking External Environment - Default - Open Loop - Optimized - Commanded - Prior History - Instructed -Wander Route Desired Line Steering Command Vehicle States -Best Line - Lane Switching - Traffic -Speed Goal Wheel Position (force) Acceleration Velocity Position Hazard Monitoring Threats Disturbances Strategic Factors X = (Goal, Subsequent Planned Trajectory, Current Target State, Acceleration, Velocity, Position) Intent Observability States ? Roadway ? Indicator Lights – Break Lights – Turn Signals – Stop Lights ? Acceleration States ? GPS Routing ? Head Position ? Dynamic History ? Tracking Behavior Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage “Master Caution” System “Master Caution” Architecture Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage 300M Face Analysis ? Driver Internal State – Vigilance –Stress ? Driver Habits – Scan Patterns 300M Pupil Tracking Image with On-Axis LEDs on Image with On-Axis LEDs off IBM BlueEyes Camera Difference Image Approaches to Enhancing Focus in the Information Rich Cockpit (cont.) ? Enhanced Perception – IR/MMW Radar (eg Cadillac Night Vision System) – Multidimensional video (“super mirror”) – Prioritized audio ? Situation Awareness Displays – Datalink ? Alerting Systems ? Advanced Internal Diagnostics Architectures – “Master Caution” ? Driver Condition Monitoring ? External Visual Systems – Active Signage Discussion Aerospace Experience ? Drive by Wire – Criticality - Fault Tolerance ? B-777 example ? Collision alert criteria – Alert vs.. autobrake ? F-16 example – Complex threat field – False alarm issue ? System Operations Curves ? HUD applications – Limited FOV ? Runway, alignment, ? Gunsight applications – Visual Accommodation ? Infinity Optics ? Visual anomalies – e.g...Lack of Fusion ? Situation Awareness (SA) displays – Testing Methods Technology Migrating into Automobile Cockpits ? Mobile Communications (Voice, Data) ? Portable Devices (Cell Phone, PDA, Wireless) – Not Controlled by Automobile Industry ? Entertainment / Info Systems (CD, Web) ? Navigation and Guidance (GPS, DGPS) ? Advanced Displays (Flat Panel, HUD) ? Sensors (Radar, IR, MEMS) ? Databus Architectures (CAN,AIRINC) ? On-board Processors (Embedded, Auto-PC) ? Control augmentation (ABS, Cruise C) ? ... Background: Current Systems ? Information Structure: – Distance to turn – Street names – Heading ? Interface: – Moving maps –Icons – Voice instructions Current System: Phillips Carin ? Uses maps, icons, and voice commands to guide the driver Direction Study Conclusions ? Humans and navigation systems both use street name and direction of turn to describe the action ? They differ in the method used to warn the driver of the upcoming action – Humans rely on external reference points: landmarks, stoplights – Navigation systems use distance & heading Data ? Subjective feedback – Cooper-Harper rating scale evaluation – Best & worst features evaluation – Comparative rating of systems ? Observations – Errors, comments, body language ? Measurements – Position, velocity Error Example: Jamaica Pond Trajectory 640 642 644 646 648 650 652 654 1940 1950 1960 1970 1980 1990 2000 2010 Latitude Longit ude Carin Map K2 Results: System Ratings 0 1 2 3 12345678910Rating N u m ber of S u bj ec t s K2 0 1 2 3 12345678910Rating N u m b e r of S ubj e c t s Carin 0 1 2 3 12345678910 Rating N u m b er of S u bj e c t s Map Obstacle Collision Alerting System Example ? Examine effect of design parameters on performance – sensor accuracy – operator response – braking deceleration ? Performance shown using SOC curves ? Monte Carlo simulation used to estimate probabilities – v = 35 mph (56 km/hr) – Safety evaluation ? Avoidance trajectory: – variable delay, 16 ft/s 2 deceleration (0.5 g) False Alarm Estimation ? Was an alert unnecessary? ? What would have happened without the alert? – Require Nominal trajectory – Definition of false alarm is situation- and operator-specific – Baseline: ? If collision would occur after 1.5 s delay, 10 ft/s 2 deceleration (1/3 g), then an alert is necessary ? Otherwise, an alert is a false alarm Implications ? A single decision threshold for all users will not be acceptable – uncertainties in response time and braking dominate – some users will experience apparent false alarms – some users will experience apparent late alarms ? Automating the braking response could improve safety – less uncertainty in response delay and deceleration profile – may still be prone to perceived false alarms – may encourage complacency, over-reliance What Will Ultimately Control Secondary Task Levels ? ? Market Forces tend to increase complexity – Market Values functionally >> complexity ? Industry Practice ? Regulatory Action ? Litigation ? Insurance ? Public Awareness ? Pressure for action ? We don’t have sufficient data to support rational action at this point Transportation Systems Level ?Fleetmanagement – Monitoring – Dispatch – Reporting – Support ? Personal vehicle management – Teenage driver monitoring – Transponders “fast pass” – Enforcement ? Passenger vehicle as part of distribution network – Low end e-commerce ? Drive through pick-up – Food – Retail – Services ? Active ride share matching Careful Formatting Turns Data Into Information Information distinguished by: ? Location ? Labeling ? Shape coding ? NOT color To help pilots identify information: ? Consistent use of shading ? Consistent use of color Every pixel earns its way on the display Formatting, not color, used to distinguish information … Surprising thing is NOT color Originally, in the early days of CRTs, we did not use color because one failure mode of the CRT is reversion to B&W. But we liked the human factors benefit we received from the additional clarity so we kept the philosophy on the LCD. We don’t use color as the only means to distinguish information. Color helps the pilot locate information but not to distinguish it. Shading is also used to help pilots identify certain information. So on these altitude tapes notice: - Boeing tape does not use white outlines because they are not necessary - The box shape is as simple as possible - Gaps between numbers and lines are intentional, to separate the information - color is used sparingly and consistently I have a couple slides on shading, And a couple slides on color. External Vision – AC 25.773-1 777 AC ? Vision Polar ? 3-Second “Rule” Also: ? Precipitation clearing ? Post widths ? other details 2.5 deg glide slope 100 ft 1200’ Runway Visual Range Down-vision angle Vref Max landing weight Forward cg 10 kt crosswind > 3 sec Vision polar and 3 second rule drive airplane configuration (approach attitude and speed) Synthetic Vision Picture of the outside world created by combining precise navigation position with databases of comprehensive geographic, cultural and tactical information. Chrysler 300M Research Platform Cooperative Effort DaimlerChrysler Motorola CC++: MIT Chrysler 300M CC++ 300M Driver Study Apply lab experience in Media and Human Interface technologies Build vehicle platform to develop and test driver behavior Develop information workload manager Identify Vehicle Motion (stop, turn, accel) Location Aware (speed, position, …) Monitor In-vehicle Situation (mood, cognitive load) Develop Tangible Interfaces (transfer info to human) Identify Driver Behavior and Stress Level Vehicle Thinks! Controls Flow of Information (warnings, phone, etc…)