Advanced Vehicle Control and Safety

8/13/2019 Advanced Vehicle Control and Safety

http://slidepdf.com/reader/full/advanced-vehicle-control-and-safety 1/86

Advanced Vehicle Control and

Safety

Chuck Thorpe

Carnegie Mellon University



1. The Problem

• Vehicles and highways have greatly improved safety: total

fatalities are down approximately 30% over the past 35

years

• Even with those improvements, there are still

approximately 40,000 fatalities / year

• People haven’t improved: in 90% of all accidents, the

driver is a contributing cause



The Solution

• The Intelligent Vehicle Initiative (IVI) is to use advancedelectronics to improve vehicles, with the dominant concern

being safety.

• This tutorial is arranged around a series of advancedfunctions, such as vehicle detection, that contribute to saferand more intelligent vehicles. For each function, thetutorial discusses a set of possible technologies.

• The next set of slides show the “user services” for the IVIadvanced vehicle control and safety systems. Thefollowing charts show which technology functions supporteach user service.

• Note the synergy: each technical function supports many

user services.



IVI User Services categories:

• Safety: (directly contributing to vehicle safety);

– rear end collision warning

– roadway departure warning

– lane change / merge collision warning

– intersection collision warning

– railroad crossing collision warning

– vision enhancement

– location-specific warnings

– collision notification

• Safety Impacting: (potential to distract or aid the driver);

– navigation and routing

– real-time traffic information

– driver comfort and convenience features



More Services

• Commercial Vehicle Services:

– vehicle stability

– vehicle diagnostics

– driver condition monitoring – cargo identification

– automated transactions

– safety recorder

• Transit:

– obstacle and pedestrian detection

– precision docking

– passenger monitoring

– passenger information



More Services

• Specialty Vehicles:

– full automation

• Supporting Services:

– low friction warning – longitudinal control

– lateral control



Technical functions

• There is a set of common vehicle functions that underliethose user services:

– sensing the position of other vehicles

– sensing obstacles – sensing the position of the lane relative to your own vehicle

– sensing vehicle position and motion

– estimating braking performance

– communication

– reliability

– miscellaneous functions

– sensor-friendly vehicles and roadways

• The rest of this section shows how each of these functionssupports the various user services



Safety 1

Other

Veh.

Obst. Lane

Pos

Con-

trol

Pos +

mtion

Brake Comm Relia

bility

Misc. Clutter

Rear

End Road

Dep Lane

Chn e Inter-

Sect

RR



Safety 2

Other

Veh.

Obst. Lane

Pos

Con-

trol

Pos +

mtion

Brake Comm Relia

bility

Misc. Clutter

Vision

Enhce Loc-

spec Coll

Notif Smart

restrnt



Safety Impacting

Other

Veh.

Obst. Lane

Pos

Con-

trol

Pos +

mtion

Brake Comm Relia

bility

Misc. Clutter

Nav/

Rout

R-T

traffic Driver

Comf.



Commercial Vehicle

Other Veh.

Obst. LanePos

Con-trol

Pos +mtion

Brake Comm Relia bility

Misc. Clutter

Stability

Driver Cond.

Vehicle

Diag.

Car o ID

Auto

Transact.

Safetyrecorder



Transit

Other Veh.

Obst. LanePos

Con-trol

Pos +mtion


Misc. Clutter

Obst /Ped Prec.Dock PassMntr

Pass

Info



Specialty

Other Veh.

Obst. LanePos

Con-trol

Pos +mtion


Misc. Clutter

Full

Auto



Supporting

Other Veh.

Obst. LanePos

Con-trol

Pos +mtion


Misc. Clutter

LowFriction LongCtrl Lat

Ctrl



Section 1 Questions:

• How many accidents occurred in the most recent year for

which statistics are available? Hint -

http://www.ohs.fhwa.dot.gov/info/saffacts.html and

http://www.census.gov/statab/www/

• How many fatalities?

• What was the dollar cost of those accidents?

• What kind of economic justification is there for the various

AVCSS services?

• Are there other on-vehicle functions that would be useful?

http://www.ohs.fhwa.dot.gov/info/saffacts.html

http://www.ohs.fhwa.dot.gov/info/saffacts.html



2 Sensing Other Vehicles

• Other vehicles need to be sensed in front for

adaptive cruise control and forward collision

warning; on the sides, for blind spot and lanechange / merge warning; and behind, for backup

warning and for lane change / merge warning of

overtaking vehicles.

• Sensing has to work in all weather, and at avariety of ranges



2.1 Basic Geometry

Sensing straight ahead is

not sufficient; on a

curving road, a forward-

looking sensor needs tohave a wide field of

view, and sensed vehicle

position needs to be

combined with road

geometry to know

whether the lead vehicle

is in your lane, another

lane, or on the shoulder.



2.2 Targets and Clutter

• Other objects in the field of view can include roadside

signs, parked cars, overpasses, guard rails, etc; this is

referred to in the radar literature as “clutter”.

• Adaptive Cruise Control (ACC) systems, which are only

concerned with moving vehicles, can reject any stopped

object as clutter.

• Rear-end collision warning systems need to sense stopped

vehicles, and so need high-acuity sensing of vehicles andlanes in order to separate targets (other vehicles) from

clutter.



2.3 Radar

• Radar is an excellent choice for seeing big metal objects through fog,

snow, or light rain

• The currently approved frequency is 77 GHz. Radar works at the speed

of light, so sensing is almost instantaneous.• Simple radar is be a single spot with no information on bearing angle.

More sophisticated versions sweep the beam mechanically, or use two

or more beams and various processing schemes to measure bearing and

range

• Typical resolution (closest objects that can be distinguised) is 1 meterin range, 3 degrees in bearing.



Radar DataData from a scanning radar.

Top image is video of thescene, bottom is radar data,

with corresponding locations

marked. The radar data is range

(horizontal) and bearing angle

(vertical; up is left, down isright). Brightness indicates

strength of return. Car A is

close and he center of the radar

return (the video image does

not extend as far to the right as

the radar); B is further and left;

C is further yet and is barely

visible above the roof of A; D

is much further and has a

bearing between A and B.



2.4 Ladar

• Ladar, lidar, and laser rangefinder are all synonyms. They refer to

measuring distance using the travel time of a laser beam. The laser can

be scanned over the scene with mirrors to produce a “range image”.

• Lasers can be focused to very small spots (fractions of a degree), sothey have much better resolution than radar. Instead of sensing a blob

with radar, a ladar can make many measurements as it scans, and can

measure fine details of shape.

• Since ladar is near visible light, it is blocked by the same kinds of

effects that impede human vision: fog, snow, and heavy rain will blockthe signals.



Ladar Data

• The figures on the next page show data from a high-resolution

scanning laser rangefinder. Each picture is 480,000 pixels (points),

each corresponding to a separate ladar measurement.

• The top picture shows the reflectance data: this is the amount of laser

energy returned from that point in the scene, and is roughly equivalent

to a flash photo.

• The lower picture shows range data. Brightness encodes range: points

that are further away are displayed more brightly.

• Note the fine details of shape and appearance visible in this data. It is

possible to build a computer program that can identify which objects

are cars, and which direction they are facing; this can give early

warning of which vehicles may be on a collision course.



Ladar Data



2.5 Sonar

• Sonar works by measuring the time of flight of sound.

• Sound travels (relatively) slowly though air and is hard to

focus, so sonar is only useful for detecting objects at

ranges of a few meters or less.

• Sonars are inexpensive, and work in a most weather

conditions. The initial mass market application was in

Polaroid auto-focus cameras.

• Sonars are commercially available for blind spot sensors

and back-up warning sensors.



Side and Rear Sensors

Radar

Sonars

This bus is

equipped

with rear andside sensors

for blind

spot

coverage



2.6 Communications

• If all vehicles on a roadway are equipped with ITS features, inter-

vehicle communications can be used to determine relative positions.

• Each vehicle can broadcast its current location, derived from GPS or

other positioning systems.• Vehicles can also broadcast other information, such as speeds, intent to

change lanes, or onset of emergency braking. This is crucial in

decreasing inter-vehicle spacing to increase throughput, while

maintaining safety.

• This kind of scheme is most appropriate for high-end IVI systems,such as automated highways.

• The picture on the next page shows a “platoon” of tightly-spaced

automated vehicles, developed by the PATH program at UC Berkeley.

Platoons rely on communications 20 times a second to keep all

vehicles moving smoothly together.



Platoon



2.7 Driver models

• Sensing the current location of a nearby vehicle is not all:

it would be even better to predict future actions of the

vehicle. Unless that vehicle is fully automated, it is

necessary to model the behavior of that driver.

• As shown in the next slide (and as everyone knows from

personal experience), there is a great deal of variability in

people’s driving behavior.

• If a particular vehicle can be observed for some time, thatdriver’s behavior can be estimated, and used to predict

future actions.



Driver Differences

The five drivers plotted

here each have

different behaviors for

one important

component of driving:average lane position.

They have different

mean lane positions

when the road is

straight, and cut thecorners by different

amounts when the road

curves

Left curve Straight Right curve




• What are the advantages and disadvantages

of using radar vs. ladar?

• The speed of light is about 3*10^8 m/sec,or, for a rule of thumb, a foot / nanosecond.

How long does it take a radar pulse to go to

and from an object 150 m away?• Find two manufacturers of automotive or

truck radars on the www



3 Sensing Obstacles

• Obstacle detection is much more difficult than

vehicle detection: obstacles can be small, non-

metallic, and much harder to see• Obstacles can be stationary or moving (e.g. deer

running across the road)

• For a passenger car at highway speeds, obstacles

need to be detected 100 m ahead. For trucks, the

distance is even longer.

• Obstacle detection is one of the most challenging

tasks for an intelligent vehicle



3.1 Obstacles on the Road

State DOTs report cleaning up construction debris, fuel spills, car

parts, tire carcasses, and so forth.

State highway patrols receive reports of washing machines, other home

appliances, ladders, pallets, deer, etc. A survey commissioned by a company that builds litter-retrieval

machines reports 185 million pieces of litter / week.

Rural states report up to 35% of all rural crashes involve animals,

mostly deer but also including moose and elk as well as farm animals.

A non-scientific survey of colleagues indicates that people have hit tirecarcasses, mufflers, deer, dogs, even a toilet.



3.2 Sensors

• Ladar, in its high-resolution scanning formats, is

useful for seeing small objects

• A variant is to use the reflectance channel of aladar, and to look for bright returns, which

probably come from objects sticking up out of the

roadway.

• Sonar has insufficient range

• Advanced radar and stereo vision systems may

work



3.3 Polarimetric radar

• Radar can be polarized in the same was as light.

• Just as polarized sunglasses help reduce light reflectedfrom shallow angles (glare), polarized radar transmitters

and receivers can separate the return from different polarization directions; this provides cues to distinguishhorizontal surfaces and from vertical surfaces.

• Polarimetric radars built at U of Michigan are much betterthan ordinary radar at separating small obstacles from

ground clutter.

• There is also some evidence that polarimetric radar willgive different returns for wet or snowy roads, giving someinformation on road conditions.



3.4 Stereo vision

• Stereo works by finding the same point in

two or more cameras. Intersecting the lines

of view from the cameras gives the 3Dlocation of the object.



Stereo Guided Segmentation

• Low-resolution stereo for detection and recognition of nearby objects,

used for side-looking sensors on a bus.

• Left: Original image. Center: depth map from stereo; brighter is close.

Right: “blobs” of pixels at the same distance. The overlays on theoriginal image show detected objects, two pedestrians and a car.

• Further processing can examine each blob to separate people from

fixed obstructions, and generate appropriate driver warnings



Long-Range Stereo

Top: One of three images

from a stereo set. The

objects on the road are 15

cm tall at a range of 100m from the camera.

Bottom: detected objects

in black. Besides the

obstacles on the road, thesystem has found the

person, the sign, grass

along the road, and a

distant dip in the road




• Look up the connection between posted speeds

and vertical curvature in the AASHTO handbook.

Is the line of sight for a human driver, going overthe crest of a hill, better or worse than for a sensor

mounted in the front bumper?

• For extra credit, go out and run over obstacles

with your car, and decide what is the largest objectyou would be willing to hit, and therefore the

smallest object that needs to be detected.



4 Sensing Lane Position

• Knowing lane position is necessary for

automated guidance and for lane departure

warning systems. It is also important forrear-end collision warning, to know which

lane your vehicle is in as well as which lane

preceeding vehicles are in.• Requirements are somewhat different for

each application.



4.1 Requirements

– reliability: high for warning systems, extremely highfor automated guidance

– availability: must be available nearly 100% for

automated guidance; lower availability acceptable forwarning systems provided a warning is given

– weather: should operate in most weather, warn anddisable if not operating

– accuracy: absolute accuracy of better than 30 cmneeded; no high-frequency jitter allowed for controlapplications

– range: rear-end warning requires knowing lane positionof leading vehicle, to approx. 100m



4.2 MagneticsUC Berkeley has pioneered the use of

permanent magnets, buried in the center

of the road, for lateral guidance. The

magnets can be inexpensive magnets, as

shown here, for most applications; or

more expensive but much smallermagnets for bridge decks where drilling

large holes would damage the structure.

The magnets are sensed by

magnetometers underneath the front and

rear bumpers of the vehicle to providelateral position information.

The magnets can be installed north pole

up or down, providing a simple binary

code that can indicate e.g. map location.



More MagnetsAn obvious advantage of

magnets is that they are not

affected by weather. Here,

they are used to mark the

edge of the shoulder, to provide a visual indicator

to the snow plow operator.

Besides buried magnets, there are also efforts to place magnets in

lane marking tape. This would be less expensive to install, but

requires more sophisticated sensing, since the magnets are not

directly underneath the vehicle’s sensors.



4.3 Buried cables

The oldest way to perform automated

guidance, going back to the 1950’s, is

to follow a buried cable. The automated

trucks at the Westrack pavement testsite use two cables for redundancy, with

pickup coils mounted in triangular

frames at both front and back of the

truck. Buried cables are all-weather,

and the signal on the cable can be usedto send messages (e.g. “speed limit

change”). But cable installation and

maintenance are difficult.



4.4 Radar reflective surfaces• Collision avoidance radar can

be used for lateral control with

modified lane-marking tape.

• Frequency-dependent tape

properties can provide

distance and other information

• Conventional lane marking

tape (3M Corp.) punched withspecific hole pattern to provide

frequency-selective retro-

reflection

Low-Freque ncyIlluminationHigh-Frequency

Illumination

Radar-Reflective Stripe

Radar

lower fhigher f

(a)

(b)



4.5 Vision

Typical vision system for

lane tracking.

The detected position of the

solid line is shown by the blue dots; the detected

dashed line by dark and

light blue dots. Overlayed

on the image is data from

other sensors, showing the

location of radar targets:

yellow X for right lane, red

X for current lane.

Experimenter interface

shown at bottom.




• What would be the relative advantages of

magnetics vs. vision?

• What is the disadvantage of buried cables?



5 Sensing vehicle position and

motion• An estimate of vehicle motion, and position on a map, can

be used in several ways, depending on the resolution. For

example:

– coarse position (10s of meters) can be used to predict that a corneris coming up

– medium position (meters) can be used to warn a driver to slow

down, based on the design speed of the upcoming curve

– fine positioning (cm) can be used to tell if the driver is drifting out

of their lane through the curve

• Several different technologies provide ways of measuring

absolute position and motion, at a variety of resolutions.



5.2 GPS

• The Global Positioning System is a satellite-based

navigation system, originally developed by the US

military. It works by broadcasting very accurate time

signals from a constellation of orbiting satellites. Aground-based receiver can compare the times from several

satellites; the different in apparent times gives the

difference in time-of-flight of the signals from the

satellites, and therefore the difference in distance to eachsatellite. Simple geometry gives the location of the ground-

based unit and an accurate time.



More GPS

• This simple picture is distorted by two phenomena

– The US government deliberately introduces distortions

into the civilian version of the signal, in order to reduce

the accuracy of the system for potential enemies

– Local atmospheric effects refract the signals by varying

amounts

• The result is that raw GPS has an accuracy of only10’s of meters



Differential GPS

• In Differential GPS, a base station has a GPS receiver at a

known location. It continually compares its known position

with the GPS reported position. The difference is the error

caused by selective availability and atmospheric distortion.The base station broadcasts the correction terms to mobile

units. By applying the correction, the mobile units can

reduce their errors.

• The accuracy of DGPS is on the order of a few meters.



Carrier Phase GPS

• In carrier phase systems, the base station and the

mobile units watch both the broadcast time code,

and the actual waveforms of the carriers. By

counting waveforms, they can synchronize their

positions with each other to a fraction of a

wavelength.

• A good carrier-phase system, with goodconditions, can achieve accuracies of 2 cm or

better.



GPS Difficulties

• GPS requires a clear view of at least 4 satellites.

For aircraft applications, or in flat, open terrain,

this is not a problem.

• In mountainous terrain, or in urban canyons, GPS

signals can be blocked or (worse) can reflect from

tall objects and cause mistaken readings.

• Carrier-phase GPS is very sensitive to losing lockon the satellites, and can become confused even

going under a large road sign.



Bottom line on GPS

• GPS is very useful for many applications.

• It is not yet 100% reliable, so is not ready

for control applications.

• Research continues on filling in gaps in

GPS coverage, and integrating GPS with

other sensors, so there is hope for the future.



Maps

• Accurate position is not useful unless combined with accurate maps.

• The first generation of digital maps were produced from paper maps,

and therefore are no more accurate than the paper products. Typical

quoted accuracies are 14 meters. This is sufficient for in-vehicle

navigation systems; until more sophisticated uses arise, there is little

market demand for high accuracy.

• The next generations of maps will be produced directly from aerial

photos and verified by driving selected routes with accurate GPS, so

the accuracies will improve.

• To be completely useful, maps should have additional information,

such as design speed of curves, grade of slopes, etc. This would aid

e.g. in warning drivers of excessive speed when entering a curve.



5.3 Inertial

• Inertial sensing measures acceleration, then integrates

acceleration to give velocity and again to give position.

• Since position is doubly-integrated, small errors in

acceleration build up rapidly.

• Inertial measurement is good for sensing braking forces or

for comparing wheel speed with ground speed and

calculating slip during braking.

• High-precision inertial navigation is not yet affordable forthe automotive market.

• Inertial measurement is useful to fill in short-term gaps in

GPS or other measurements.



5.4 Other sensors

• “Dead reckoning” uses estimates of distance travelled and

direction of travel.

• Odometry uses wheel encoders to measure distance

traveled. It is susceptible to errors due to tire slip, incorrectestimates of wheel circumference due to changes in tire

inflation, etc. Road Rally enthusiasts can calibrate their

odometry to 0.1%; this is not practical for most vehicles.

• Standard compasses are affected by nearby metallicobjects, such as bridges or buildings.



More Sensors

• Image correlators directly measure vehicle motion by

watching the ground move by under the vehicle. These

systems are accurate to better than 0.1%

• Doppler radar is used in precision agriculture applications,where it is important to measure the speed of farm

equipment even with significant tire slip.




• Why can’t you just use a magnetic compass

for heading?

• What’s the cheapest GPS unit you can findon the web?

• Why would Japan have a higher market

penetration of GPS and moving mapdisplays than the US?



6 Predicting Braking

Performance• Braking performance is key to setting many

parameters in automated control and in driver

warning systems.

• To set safe following distance, ideally the system

should know its own braking capability; the

braking capability of the lead car; and the reaction

time of the automated system or of the• Braking performance of vehicles on identical

roadways can vary by a factor of 4



6.1 Basic formulas

• The basic formulas for the time and distance required to

bring a car to a stop are

• Time = reaction time + speed / deceleration

• Distance = speed * reaction time + ½ speed 2 / deceleration

• Typical highway speeds are approximately 30 meters /

second; typical reaction times range from 100 milliseconds

for a fast computer-controlled sensor and brake actuator, to

up to 2 seconds for a human driver. The dominantunknown factor is deceleration, or braking performance.



6.2 Wheel speeds and slip

Force(g)

Slip (%)

Dry surface

Wet surface

Typical force vs. slip curve. As the

brakes are applied, the tires begin to slip,

which results in deceleration force. As

the slip increases, the force increases to

some maximum. After that point, thewheels begin to lock and skid, and the

braking force decreases. Note that the

curves for wet and dry pavement start

off very close to each other, but reach

different peaks. This means that gentlytapping the brakes is not enough to tell

surface type, and therefore it is difficult

to predict maximum braking

performance without attempting hard

braking.



6.3 Surface condition sensing

• Several methods have been attempted to sense current

surface conditions:

– infrared spectrophotometers, tuned to detect differences between

ice, water, and dry pavement – microphones in the wheel wells listening for water splash sounds

– roadside mini-weather stations with sensors built into the pavement

– careful instrumentation of all wheels of a car, looking for incipient

slip on the driving wheels

• None of the methods is completely successful yet.




• Have you ever encountered “black ice” that you

couldn’t tell was there?

• Calculate stopping distance for the following parameters: – Truck with 1.0 sec reaction time and 0.3 g braking

– Sedan with 1.0 sec reaction time and 0.7 g braking

– Sedan with sleepy driver, 1.5 sec reaction time and 0.7 g braking

– Sedan with poor brakes, 1.0 sec reaction and 0.5 g braking

– Sports car with professional driver, 0.5 sec and 1.0 g

• Which factors dominate stopping distance?



7 Reliability

Reliability engineering in intelligent vehicles is

difficult. Several characteristics of automobiles

are much different than, e.g., aircraft:

Cost sensitivity: Usual practices that involve triplex

redundancy of critical components may not be

affordable in automobiles.

Equipment used until end-of-life: In most safety-critical tasks, preventive maintenance schedules call

for replacing electronics before the end of their

design life. In the automotive environment, many

components are never replaced until they fail.



More Reliability

Operation in uncontrolled environment: Vehicles

operate in harsh environments, with relatively

unskilled and untrained operators.

Very large scale of deployment: An extremely

improbable event, one that occurs once in 109 hours,

would cause one failure in 73 years in the US

commercial air fleet. That same probability would

cause a failure once every 4.5 days in the USautomotive fleet, due to the much higher number of

vehicles. Even though the risk to a passenger might

be the same in both cases, the public perception of

risk could be much higher for cars.



7.1 Redundancy

• Duplex redundancy refers to having two copies of a

subsystem (e.g. computer). If a failure is detected in one

system, the other can be used.

• Triplex redundancy has three copies. for computers, theoutput of all three can be compared, and the majority wins;

this provides automatic detection and correction of single

errors.

• Heterogeneous redundancy refers to doing the samefunction with different means. For instance, if a steering

actuator fails on an automated vehicle, some steering

authority is available by differentially applying the right or

left brakes.



7.2 System-level solutions

• System level solutions build safety into the

system by considering the entire system. In

automated highways, the California PATHapproach of Platoons is designed to mitigate

the effects of an (unlikely) crash by having

vehicles so closely spaced that any collision

would be at a small relative velocity.



Questions:

• How reliable is your car? Your computer?

Would you trust your life to them?

• Describe heterogeneous redundancy, andgive an example.



8 Emerging technologies

• A number of other technologies are being

developed that will support intelligent vehicles.

• Some, such as electronic controls, are beingdeveloped for other purposes (e.g. handling), but

will be useful for intelligent vehicles.

• As drivers become more accustomed to electronics

in vehicles, prices will fall, consumer acceptancewill increase, and the pace of adoption of new

technology could accelerate.



8.1 Control

• Current IVI applications are focused on driver assistance

rather than vehicle control; nevertheless, partial and full

automation will eventually be important.

• A wide variety of standard and advanced controlstechniques are being applied to road vehicles

• Vehicles to date have been designed for human control, not

automated control. For example, current steering system

geometry is designed for “good handling”, i.e. predictableresponse for humans. The underlying hardware may need

to be modified for optimal automatic control.



Difficulties

• Automated control is especially difficult in some

situations:

Emergency maneuvers: Control systems optimized for smooth

performance at cruise will not work for abrupt maneuvers inemergency situations.

Equipment failure: Special controllers need to be designed to cope

with tire blowout or loss of power brakes or power steering.

Heavy vehicles: The load, and the distribution of the load, vary

much more for a heavy truck than for a passenger car. Truck

controllers need to be much more adaptable than light vehicle

controllers.



More Difficulties

Low speeds: Engine and transmission dynamics are hardest to

model at slow speeds. Applications such as automated snow plows

or semi-automated busses will require careful throttle control

design.

Low-friction surfaces: As addressed above, it is difficult to predict

the effective coefficient of friction on a particular road surface.

This affects not only braking performance but also the design of

throttle and steering controllers.



8.2 Actuation

• Full or partial automation will require actuators, i.e.

computer-controlled motors that can move the throttle,

brake, and steering.

• The state of the art is rapidly improving: vehicles areavailable on the market with electronic fuel injection,

electronic power steering, and electronic power brakes, all

driven by performance and weight improvements for

manually-driven cars. This makes it much easier to addcomputer control.

• Special-purpose actuators will still be needed in some

applications, such as quick-response throttles for closely-

spaced platoons of cars.



8.3 Driver condition

• It is important to assess driver alertness,

both in a drowsy driver warning system,

and in an automated system that is preparing to return control to the driver.

• Alertness can be sensed indirectly, by

watching lane-keeping performance; ordirectly, by watching for eye blink rate and

closure.



Perclose

Measuring percentage of time

eyes are closed. This system

illuminates the face with two

IR wavelengths, one of whichreflects from the retina.

Subtracting the images will

create a blank image (if the

eyes are closed) or an image

with two bright spots (if the

eyes are open).

Top left: image with retinal reflections

Top right: no retinal reflections

Bottom left: difference image, note

two bright dots for reflections



8.4 Communications

• Infrastructure-based ITS projects are building roadway-to-vehicle

communications for traffic and routing information.

• Dedicated Short Range Communication (DSRC) is being developed

for warning of local conditions, such as ice, sharp curves, changes in

speed limit, or stopped traffic out of sight around a bend.

• Vehicle-vehicle communications will be increasingly important for

collision warning systems. The lead vehicle can communicate speed,

braking, intent to change lanes, traffic status ahead, etc.

• In the platoon version of full automation, vehicles need to

communicate with low latency, e.g. 20 times / second. This creates

interesting research questions on creating local nets, on managing both

inter- and intra-platoon communication, and on reliable

communications in urban canyons and other difficult environments.



Communications Technologies

• Most communications schemes rely on radio,

using a variety of bands.

• Schemes currently under research include: – modulating the radar reflectivity of a surface, so radar-equipped

trailing vehicles can get information as well as range

– powering a transponder with radar energy, again to communicate

to a following radar-equipped vehicle

– modulating LED brake lights so trailing vehicles equipped withdetectors tuned to that particular wavelength can pick up

information




• What makes vehicle control difficult?

• What makes communications difficult?



9 Sensor-friendly roadways

and vehicles• On-board sensing would work better if the

environment were designed for sensing.

• Current roadways have significant variability(Bott’s dots, painted lines, thermoplastic stripes,

etc).

• Current roadways have many objects that cause

radar “clutter” (returns from objects that are not ofinterest), such as guard rails, roadside signs,

bridge overpasses



9.2 Path prediction

• “Path Prediction” refers to estimating where the

vehicle’s current lane goes, so an obstacle

detection system knows where to look for stopped

cars and other obstructions.

• Sensor friendly systems will improve path

prediction by enhancing lane visibility.

• They will also improve obstacle detection byreducing clutter from off-road objects and

increasing returns from other vehicles.





Sensor-Friendly Features

• Besides clutter suppression, sensor-friendly

systems can improve visibility:

– Lane markings can be improved with pigmentsthat reflect radar or near-visible wavelengths

– Vehicle visibility can be improved with radar

reflectors, either fixed or modulated for

communications

Mi i h fl



Microstrip patch retroreflector

antenna• Without a stable aiming

point, radar-based vehicle

tracking is difficult. Lead

vehicle appears to wander • OSU patch retro-reflector

provides a distinctive,

wideband, vehicle marker.

Compact form factor is easily

attached to vehicles.

• Angle-invariant return provides

aim-point stability.

• Wide bandwidth permits good

range resolution. F r e q .

[ G H z ]

Angle




• List four ways of handling clutter.

• How can sensor-friendly features help with

the path prediction problem?

10 C d l i



10 Comments and conclusions

• Many of these technologies work best in combination: e.g.

lane tracking aids both lane departure and rear-end

warnings.

• Many of these work best with some infrastructureassistance: e.g. lane departure systems need at least good

road delineation, and can take advantage of better

markings.

• In many cases, the technology is approaching readiness;the remaining obstacles to deployment are legal and

institutional.



Acknowledgements

• My thanks to the CMU Navlab group, and the Automated Highways

Tech Team. Much of the research described here was supported by

NHTSA and FHWA.

• Photo credits: thanks to Liang Zhao (stereo), Todd Williamson

(stereo), Richard Grace (perclose), Gerald Stone and California PATH

(magnets and snowplow), Bill Stone and California PATH (platoon),

Colin Ashmore (buried wire), Umit Ozguner, Jon Young, and Brian A.

Baertlein (radar reflective surfaces and microstip antenna), K2T Inc

(ladar), Dirk Langer (radar), Parag Batavia (driver differences chart),

Assistware Technologies (vision system) and Todd Jochem (bus). All pictures copyright by their owners; reproduced by permission.

Documents

Advanced Vehicle Control and Safety