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Advanced Vehicle Control and Safety

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1 Advanced Vehicle Control and Safety
Chuck Thorpe Carnegie Mellon University

2 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 in the US People haven’t improved: in 90% of all accidents, the driver is a contributing cause

3 The Solution The Intelligent Vehicle Initiative (IVI) is a USDOT program to use advanced electronics to improve vehicles, with the dominant concern being safety. This tutorial is arranged around a series of advanced functions, such as vehicle detection, that contribute to safer and more intelligent vehicles. For each function, the tutorial discusses a set of possible technologies. The next set of slides show the “user services” for the IVI advanced vehicle control and safety systems. The following charts show which technology functions support each user service. Note the synergy: each technical function supports many user services.

4 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

5 More Services Commercial Vehicle Services: Transit: vehicle stability
vehicle diagnostics driver condition monitoring cargo identification automated transactions safety recorder Transit: obstacle and pedestrian detection precision docking passenger monitoring passenger information

6 More Services Specialty Vehicles: Supporting Services: full automation
low friction warning longitudinal control lateral control

7 Technical functions There is a set of common vehicle functions that underlie those 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 functions supports the various user services

8 Safety 1

9 Safety 2

10 Safety Impacting

11 Commercial Vehicle

12 Transit

13 Specialty

14 Supporting

15 Section 1 Questions: How many accidents occurred in the most recent year for which statistics are available? Hint - and 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?

16 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 lane change / 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 a variety of ranges

17 2.1 Basic Geometry Sensing straight ahead is not sufficient; on a curving road, a forward-looking sensor needs to have 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.

18 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 and lanes in order to separate targets (other vehicles) from clutter.

19 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 meter in range, 3 degrees in bearing.

20 Radar Data Data from a scanning radar. Top image is video of the scene, bottom is radar data, with corresponding locations marked. The radar data is range (horizontal) and bearing angle (vertical; up is left, down is right). 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. A B C D

21 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), so they 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 block the signals.

22 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.

23 Ladar Data

24 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.

25 Side and Rear Sensors Sonars Radar
This bus is equipped with rear and side sensors for blind spot coverage

26 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.

27 Platoon

28 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, that driver’s behavior can be estimated, and used to predict future actions.

29 Left curve Straight Right curve
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 the corners by different amounts when the road curves Left curve Straight Right curve

30 Section 2 Questions: 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

31 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

32 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 tire carcasses, mufflers, deer, dogs, even a toilet.

33 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 a ladar, 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

34 3.3 Polarimetric radar Radar can be polarized in the same was as light. Just as polarized sunglasses help reduce light reflected from shallow angles (glare), polarized radar transmitters and receivers can separate the return from different polarization directions; this provides cues to distinguish horizontal surfaces and from vertical surfaces. Polarimetric radars built at U of Michigan are much better than ordinary radar at separating small obstacles from ground clutter. There is also some evidence that polarimetric radar will give different returns for wet or snowy roads, giving some information on road conditions.

35 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 3D location of the object.

36 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 the original 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

37 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 100 m from the camera. Bottom: detected objects in black. Besides the obstacles on the road, the system has found the person, the sign, grass along the road, and a distant dip in the road

38 Section 3 Questions: Look up the connection between posted speeds and vertical curvature in the AASHTO handbook. Is the line of sight for a human driver, going over the 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 object you would be willing to hit, and therefore the smallest object that needs to be detected.

39 4 Sensing Lane Position Knowing lane position is necessary for automated guidance and for lane departure warning systems. It is also important for rear-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.

40 4.1 Requirements reliability: high for warning systems, extremely high for automated guidance availability: must be available nearly 100% for automated guidance; lower availability acceptable for warning systems provided a warning is given weather: should operate in most weather, warn and disable if not operating accuracy: absolute accuracy of better than 30 cm needed; no high-frequency jitter allowed for control applications range: rear-end warning requires knowing lane position of leading vehicle, to approx. 100m

41 4.2 Magnetics UC 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 smaller magnets 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 provide lateral position information. The magnets can be installed north pole up or down, providing a simple binary code that can indicate e.g. map location.

42 More Magnets An 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.

43 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 test site 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 used to send messages (e.g. “speed limit change”). But cable installation and maintenance are difficult.

44 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 with specific hole pattern to provide frequency-selective retro-reflection

45 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.

46 Section 4 Questions: What would be the relative advantages of magnetics vs. vision? What is the disadvantage of buried cables?

47 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 corner is 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.

48 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. A ground-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 each satellite. Simple geometry gives the location of the ground-based unit and an accurate time.

49 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 only 10’s of meters

50 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.

51 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 good conditions, can achieve accuracies of 2 cm or better.

52 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 lock on the satellites, and can become confused even going under a large road sign.

53 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.

54 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.

55 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 for the automotive market. Inertial measurement is useful to fill in short-term gaps in GPS or other measurements.

56 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, incorrect estimates 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 metallic objects, such as bridges or buildings.

57 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.

58 Section 5 Questions: Why can’t you just use a magnetic compass for heading? What’s the cheapest GPS unit you can find on the web? Why would Japan have a higher market penetration of GPS and moving map displays than the US?

59 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

60 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 speed2 / 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 dominant unknown factor is deceleration, or braking performance.

61 6.2 Wheel speeds and slip 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, the wheels 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 gently tapping the brakes is not enough to tell surface type, and therefore it is difficult to predict maximum braking performance without attempting hard braking. Force (g) Slip (%) Dry surface Wet surface

62 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.

63 Section 6 Questions: 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?

64 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.

65 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 US automotive 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.

66 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, the output 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 same function 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.

67 7.2 System-level solutions
System level solutions build safety into the system by considering the entire system. In automated highways, the California PATH approach 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.

68 Questions: How reliable is your car? Your computer? Would you trust your life to them? Describe heterogeneous redundancy, and give an example.

69 8 Emerging technologies
A number of other technologies are being developed that will support intelligent vehicles. Some, such as electronic controls, are being developed 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 acceptance will increase, and the pace of adoption of new technology could accelerate.

70 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 controls techniques 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. predictable response for humans. The underlying hardware may need to be modified for optimal automatic control.

71 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 in emergency 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.

72 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.

73 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 are available 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 add computer control. Special-purpose actuators will still be needed in some applications, such as quick-response throttles for closely-spaced platoons of cars.

74 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; or directly, by watching for eye blink rate and closure.

75 Perclose Measuring percentage of time eyes are closed. This system illuminates the face with two IR wavelengths, one of which reflects 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

76 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.

77 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 with detectors tuned to that particular wavelength can pick up information

78 Section 8 Questions: What makes vehicle control difficult?
What makes communications difficult?

79 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 of interest), such as guard rails, roadside signs, bridge overpasses

80 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 by reducing clutter from off-road objects and increasing returns from other vehicles.

81 9.1 Dealing with clutter Clutter can be:
Moved: Sign posts could be placed farther from the travel lanes. Masked: Radar Absorbing Material (RAM) could be applied to objects such as bridge abutments Marked: Polarizing reflectors, or filters that absorb only a narrow frequency band, could be applied to large objects. They would then still appear in a radar return, but would be marked in the radar signal as known fixed objects Mapped: The locations and signatures of fixed objects could be stored in a map, and provided to individual vehicles.

82 Sensor-Friendly Features
Besides clutter suppression, sensor-friendly systems can improve visibility: Lane markings can be improved with pigments that reflect radar or near-visible wavelengths Vehicle visibility can be improved with radar reflectors, either fixed or modulated for communications

83 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 Angle-invariant return provides aim-point stability. Wide bandwidth permits good range resolution. Freq. [GHz]

84 Section 9 Questions: List four ways of handling clutter.
How can sensor-friendly features help with the path prediction problem?

85 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 infrastructure assistance: 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.

86 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.

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