Patent Description:
Many companies are developing autonomous vehicles for commercial and personal use on existing roadways for a variety of applications, including but not limited to personal taxi services, delivery services, and the like. In accordance with the present invention, an autonomous vehicle is a vehicle capable of operating without a human driver. Such vehicles can be designed to operate utilizing an onboard computer and a system of sensors designed to drive, steer, and otherwise operate the vehicle in the same manner as if there were a human operator. It is envisioned that fleets of autonomous vehicles will soon be available, similar to a network of taxis, buses or delivery vans, whereby a user can request an autonomous vehicle to pick-up, transport and drop off passengers, or pick-up, transport, and deliver packages or the like, on demand. Alternatively, users can own an autonomous vehicle for personal use and use it for ordinary tasks, such as commuting to work, running errands, dropping the kids off at school, for travel, or the like.

Current autonomous vehicles in the development and testing stages generally utilize multiple systems to fully operate the vehicle without a human operator. First, a standard GPS system is used to plan a route for the vehicle. Taking into account the starting point and the destination for a trip, as well as other factors such as traffic conditions, road closures, weather conditions, preferred routes, toll roads, etc., the GPS system determines the best route for the vehicle to take. However, for safe and efficient operation, autonomous vehicles also need a system to recognize dynamic conditions along the route during operation of the vehicle. Such a system may be referred to as an augmented GPS system, which utilizes an array of technologies, such as cameras, sensors, radar, LIDAR and lasers to provide a three-dimensional view around the vehicle during operation. Such a system can keep track of other cars around the vehicle; detect obstacles or hazards around the car, in the road up ahead, or approaching the car from the front, rear, or sides; and determine the location of the edge of the road or travel lane, upcoming turns, hills or descents, and assess general road conditions ahead, behind and around the vehicle. Autonomous vehicles also require a centralized system within the car to process the information provided from the GPS system and augmented GPS system and utilize the processed information to operate the vehicle. Such commonly utilized systems generally include a Computer Area Network (CAN) bus in the vehicle to communicate with and coordinate operation of the GPS system, augmented GPS system and other autonomous vehicle operating systems.

Non-autonomous vehicles also utilize similar technology to back-up a human driver. For example, cars have used various forms of cruise control for decades. More recently, cars have been equipped with systems that will autonomously parallel park the car. Many modern cars are now equipped with systems that assist the driver when the car begins to drift out of its lane on the highway, or brake the car if it is getting too close to the car in front of it, or alert the driver if there is an object in the road ahead.

Until guidance systems on-board autonomous vehicles match or exceed the perception and analytical decision-making ability of human drivers, there will be numerous ongoing daily situations which will frustrate the ability of a full autonomous vehicle to properly and dynamically respond to, or react to, its surroundings. Moreover, until autonomous vehicles can safely rely upon existing operational systems and sensors for safe and efficient operation and eliminate essentially all risks, the public will continue to be hesitant to put full faith in true autonomous operation of such vehicles. Indeed, numerous "real-world" autonomous vehicular tests have resulted in guidance failures, accidents, etc., caused by guidance systems and sensors that have failed to adequately detect, recognize and/or react in a timely fashion due to challenging ambient conditions, and as a result, most autonomous vehicle testing is usually limited to warm, sunny climate areas.

While various optically-based automotive and/or autonomous guidance systems and sensors (e.g., video, LIDAR, etc.) are capable of performing well under ideal visual and ambient conditions, their capabilities can quickly diminish to unusable levels under adverse ambient conditions, such as during or shortly after rain, snowfall, fog, etc., or when it is dark outside and in low-lighted areas of the roadway. Additionally, while the existing level of "on-board" sensors, cameras, devices, and interfaces can alter an autonomous vehicle's driving characteristics to a limited degree (e.g., by braking for unexpected obstacles and/or other vehicles, or steering a vehicle if it drifts out of its lane, or adjusting the propulsion of the vehicle, etc.), there is currently an inherent extreme deficiency in giving autonomous vehicles the ability to react properly to harsh ambient conditions, such as fog, snow, heavy winds or extreme darkness, that can confuse or render useless many optically dependent sensors. Existing GPS navigation systems alone, and high-resolution digital maps cannot be absolutely relied upon, as their databases do not cover the majority of roadways, and are constantly becoming outdated. Accordingly, there is a need to improve upon existing optically-based guidance systems and sensors to ensure that operation of an autonomous vehicle is safe and efficient in all conditions.

Accordingly, there is a need for an improved system for the operation of autonomous vehicles, as well as manually driven vehicles, to continue to properly guide themselves during conditions on a roadway that overcomes the drawbacks and limitations of existing dynamic guidance systems. Further, there is a need for a system that utilizes infra-red detection and imaging with sensors that can assist in the safe and efficient operation of vehicles in response to unexpected and unpredicted situations or conditions on a roadway, and that will aid the vehicles in determining appropriate responsive actions in a quick and expeditious manner.

<CIT> describes a driver assistance system for a vehicle. The system comprises a video camera mounted on a vehicle and used to detect the lane markings on a road.

<CIT> describes a method and control unit in a vehicle for estimating a stretch of road based on a set of tracks of another vehicle.

<CIT> describes an infrared imaging system that comprises a far-infrared camera disposed at the front end of a vehicle adapted for detecting thermal radiation and producing an image signal indicative of the temperature of the surrounding objects.

The present invention is generally directed to a passive infra-red guidance system for augmenting the operation of autonomous vehicles on a roadway. In accordance with embodiments of the present invention, the system provides a superior approach to assist a vehicle or driver in finding and determining the center point of an established travel lane when the roadway visibility, in general, is low, less than optimal, or otherwise compromised, and when the performance of other navigation systems may be diminished or ineffectual, and instantly respond to any detected guidance deviation.

Central to the operation of the system in accordance with the present invention is the use of at least one forward-looking passive infra-red (IR) image sensor mounted on a vehicle that is in operative communication with an image processor tied into the operational system of the vehicle, such as via a central CAN Bus unit in constant communication with various vehicle sensors, such as the IR sensors, for analysis and output processing, preferably immediately in real-time. In operation, the image processor analyzes the radiated thermal differences between a roadway's surface and areas adjacent to the roadway, which subsequently provides a data "picture" of where a roadway and/or a travel lane exists and ends. More particularly, the image processor, based on data measured by the at least one IR sensor, establishes a left edge line and a right edge line of the roadway, and then determines a centerline for a travel lane in which the vehicle is travelling. This information may be used to provide, for instance, a "heads up" display outlined on the windshield of a driven vehicle, or as a data input to the operating and/or navigation system of an autonomous vehicle. Though preferably used in autonomous vehicles, the system of the present invention can also be used in human-operated vehicles as an adjunct to a human driver, who, like the autonomous vehicle system, cannot properly identify the vehicle's position relative to the travel lane(s) on a snow- and/or ice-covered, or foggy, or poorly lit, or damaged roadway travel lane by optical means.

The present invention provides for a method for centrally locating a vehicle within an established travel lane on a roadway according to claim <NUM>.

The present invention further provides for a system for centrally locating a vehicle within a travel lane on a roadway according to claim <NUM>.

The system in accordance with the present invention is capable of working with both manually driven, as well as autonomous vehicles.

Objects, features and advantages of the present invention will become apparent in light of the description of embodiments and features thereof, as enhanced by the accompanying figures.

Referring to <FIG>, a first operational mode of a passive infra-red guidance system in accordance with the present invention is illustrated. As illustrated in <FIG>, a vehicle <NUM> generally travels within a travel lane <NUM> on a roadway <NUM>. The system, generally designated by reference numeral <NUM>, comprises at least one forward-looking passive IR imaging sensor or sensor array, generally designated as reference numeral <NUM>, mounted on the vehicle <NUM> and directed in outwardly front of the vehicle <NUM> so that it can identify the edges of the roadway <NUM> or travel lane <NUM>. In preferred embodiments of the present invention, as illustrated in <FIG>, a single, forward-looking IR sensor <NUM> is mounted on the vehicle <NUM>, preferably on the front of the vehicle <NUM>, and more preferably centered on the vehicle <NUM> so that it can measure both the left and right sides of the roadway <NUM> in front of the vehicle <NUM> during travel. Such a forward-looking IR sensor <NUM> would generally cover a relatively close range in front of the vehicle <NUM> - about <NUM> to <NUM> meters (<NUM> to <NUM> feet) in front of the vehicle <NUM>. Optimally, the IR sensor <NUM> has a relatively large pixel array, for example, about <NUM> x <NUM> or greater.

In alternate set-ups of the thermal imaging sensor assembly, multiple forward-looking IR sensors <NUM> can be mounted to the vehicle <NUM>, preferably in over-lapping and/or redundant fashion. In an alternate embodiment of the present invention, as illustrated in <FIG>, the vehicle <NUM> may include a dedicated right-side IR sensor 106R - directed toward the right edge of the roadway <NUM>/travel lane <NUM> in a forward-looking manner - and a dedicated left-side IR sensor <NUM> - directed toward the left edge of the roadway <NUM>/travel lane <NUM> in a forward-looking manner. In such an embodiment, the sensors 106R and <NUM> may be positioned on the front end of the vehicle <NUM> or alternately on the lateral sides of the vehicle <NUM>, and be directed forwardly from the vehicle <NUM>.

The following discussion of IR sensors in regards to the present invention could be a single sensor or a set of sensors operating to a collective end of detecting edges of the roadway <NUM>/travel lane <NUM> through thermal imaging.

Referring to <FIG> and <FIG>, the IR sensors <NUM> are in operative communication with an image processor <NUM>, such as a video processor, tied into the operational system of the vehicle <NUM>, such as via a central CAN Bus unit <NUM>. Preferably, the CAN Bus <NUM> is in constant communication with various vehicle sensors, such as the IR sensors <NUM>, for analysis and output processing, preferably immediately in real-time, based on the detected data. In operation, the system <NUM> determines the left edge and the right edge of the roadway <NUM> using thermal imaging. More particularly, the image processor <NUM> analyzes the thermal differences between a roadway's surface and areas adjacent to the roadway <NUM>, as well as roadway features, such as embedded roadway lane or centerline reflectors, etc., and subsequently creates a data "picture" of where a roadway <NUM> and/or a travel lane <NUM> exists and ends. Referring to <FIG>, the image processor <NUM> establishes a left curb line <NUM> and a right curb line 112R based on data received from the IR sensors <NUM>.

As noted, each IR sensor <NUM> preferably has a relatively large pixel array - e.g., about <NUM> x <NUM> or greater. In operation, the image processor <NUM> focuses on a subset of the pixels measured by the sensors <NUM> to identify the left and right edges <NUM> and 112R of the roadway <NUM> or travel lane <NUM>. For example, the image processor <NUM> can look at the left <NUM> pixels or so to identify the left edge <NUM> of the roadway <NUM>/travel lane <NUM> and the right <NUM> pixels or so to identify the right edge 112R of the roadway <NUM>/travel lane <NUM>. Multiple processors may be used to analyze the sensor data more quickly and efficiently, and so that both the left and right edges <NUM> and 112R can be analyzed simultaneously.

In embodiments of the present invention using multiple IR sensors, such as a dedicated left and right IR sensor <NUM> and 106R, respectively, the left and right curb lines <NUM> and 112R will be established based on respective thermal measurements from the sensors <NUM> and 106R. In this regard, a single image processor <NUM> may be in operative communication with each of the left and right IR sensor <NUM> and 106R, or alternatively, a dedicated left image processor and a dedicated right image processor may be used to determine the left and right curb lines <NUM> and 112R, respectively.

The established curb line information is supplied by the image processor <NUM> to the CAN Bus <NUM>, which establishes the centerline 112C for the roadway <NUM> or travel lane <NUM> depending on the calculated width of the roadway curbs. Upon establishing the centerline 112C, and comparing the vehicle's relative position to the calculated centerline 112C, the CAN Bus <NUM> supplies adjustment instructions to the vehicle operating and guidance systems, generally designated as reference numeral <NUM>, if such adjustments are needed. Appropriate adjustments can generally include providing direct input to a vehicle's "Driver Assist" steering system <NUM>, automatic activation of a vehicle's braking system <NUM>, or adjustment of a vehicle's propulsion system <NUM>. The information may also be provided as a data input to the navigation system of the autonomous vehicle <NUM>.

As part of the centerline establishment step, the CAN Bus <NUM> can utilize information from a GPS or navigation system supplied with information about the roadway <NUM> - such as, how many lanes the roadway <NUM> has; which lanes travel in which direction; whether the vehicle <NUM> is proximate to or nearing an exit, off ramp, or side street; how large the shoulder is - in order to accurately calculate the centerline 112C for a particular roadway <NUM> or travel lane <NUM>. In this regard, upon establishment of a left curb line <NUM> and a right curb line 112R by the image processor <NUM>, the CAN Bus <NUM> can extrapolate the proper position of the travel lane <NUM> for the vehicle <NUM> and the vehicle's relative actual position therein in order to determine if adjustments are needed to move the vehicle <NUM> left or right within the travel lane <NUM>.

While generally described herein for use in connection with autonomous - or driverless - vehicles, the system <NUM> of the present invention can also be used in driven vehicles, either having a quasi-autonomous mode or as a back-up redundancy to the human operator. For example, the centerline information and suggested corrective action may be provided, for instance, as a "heads up" display outline <NUM> on a driven vehicle <NUM>, or as a video or graphic "see-through" OLED panel, or other display method, ideally sandwiched between the layers of the windshield, or as a data input to the navigation system of the vehicle <NUM>, as illustrated in <FIG>. The driver may be able to adjust the vehicle's position and speed manually, or in the alternative, the vehicle <NUM> may automatically adjust the vehicle's position and speed based on such continuous monitoring of the vehicle's position. The "see-through" OLED panel may also be used to display other vehicle-related information from other vehicle systems.

Typical roadway surface materials present a vastly different emitted thermal characteristic from that of adjacent non-roadway materials and surfaces, and thus present a contrasting thermal picture to an IR sensor <NUM>. For example, during a snow event, an IR sensor <NUM> can make ready distinctions between the warmer pavement of the roadway <NUM> and the cooler dirt/grass/vegetation <NUM> situated on the side of the roadway <NUM>. Alternately, when the roadway curbing is made of material such as granite that has a greater thermal mass than the roadway material, then this type of roadside curbing still thermally contrasts with the roadway surface, just in the opposite direction. It is important to note that in accordance with the present invention, it does not matter what the absolute thermal reading of any area or roadway actually is, but rather the system <NUM> is looking for thermal boundary differences, however subtle, to determine where the roadway edge is located. Of additional note, the image processor <NUM> is continually and dynamically optimizing the thermal contrast range of the displayed images by utilizing outside ambient temperature data readings from the CAN Bus <NUM>. When this contrasting data is sent and processed by an image processor <NUM>, definitive roadway edge lines <NUM> and 112R can be determined and used to further determine a centerline 112C of a roadway <NUM> or travel lane <NUM>, or sent via a vehicle's CAN Bus <NUM> to be operatively connected to a vehicle's guidance system(s) <NUM> for autonomous steering, propulsion, and or braking adjustment, or, for example, to a heads-up display <NUM> superimposed on the windshield of a vehicle <NUM> for aiding a human driver.

Additionally, the application of typically used solid or liquid melting agents which are applied either before, during, or after snow/ice conditions will contrast with and further enhance the thermal signature of a roadway <NUM> relative to its adjacent areas <NUM>, and also serve as a powerful de facto initial "marker trail" for the vehicle <NUM> to follow using the system <NUM> of the present invention.

Referring to <FIG>, an alternate embodiment of the present invention, especially useful in difficult road conditions, such as snow- or ice-covered roadways, is illustrated. <FIG> essentially illustrates what an IR sensor would see, as well as what would be displayed from the image processor <NUM>, even though such tire tracks would generally be difficult or often impossible to detect in the visible wavelength spectrum as the tracks would typically be the same color as adjacent undisturbed snow, ice, or rain. In such an embodiment, the image processor <NUM> can supply a vehicle <NUM> with the ability to identify the ad-hoc "path" created by a previous vehicle's travel, or, as noted above, the prior application of melting agents if there are no prior tire tracks to create a target centerline of this path for an autonomous vehicle <NUM>, or to guide a driver. Intrinsic friction created by the constant flexing of a tire's sidewalls and tread inherently creates heat and a subsequent rise in the internal air temperature of a vehicle's tires, which transfers through the tire's tread onto a dry, rain-covered, or snow- and/or ice-covered roadway surface creating a traceable historic path for a passive IR sensor <NUM> to detect. Additionally, the pressure of a previous vehicle's tires carrying the vehicle's substantial weight during the compacting of snow, ice, or rain under the tires creates additional pathway heating for the IR sensor <NUM> to detect.

As noted, such an embodiment is especially useful when a vehicle <NUM> is travelling on a snow-covered road. Traditional active optical visual sensing systems, such as LIDAR or video cameras, would have an extremely difficult, if not impossible, time differentiating shallow tread depths in the generally monochromatic surface reflectively of a snow-covered roadway <NUM>. The system <NUM> of the present invention, by passively detecting thermal energy created in the tire tracks <NUM>, can create an ad hoc centerline in the middle of the previous tire tracks <NUM>, much as a human driver does in a roadway that has been travelled on, but not yet plowed. In the case of an autonomous vehicle <NUM>, the output generated by the image processor <NUM> is sent to the vehicle's steering system <NUM> such that appropriate corrections can be made in the vehicle's operation. In the case of a driven vehicle <NUM>, guidance information can be provided on a Heads-Up display <NUM> to assist the driver, such as a calculated and/or suggested ad hoc centerline projected on the windshield or left/right guidance arrows. With light snow cover and/or no recent vehicle travel (such that there are no prior tire tracks to follow), the system <NUM> can revert to measurement of the left and right curb lines <NUM> and 112R, such as discussed above. However, with heavy snow cover and/or recent travel on the roadway <NUM>, such an alternate centerline determination method can be used for safe and efficient operation of the vehicle <NUM>. Since the system <NUM> does not utilize visible light wavelengths, its operational ability is exactly the same day or night.

In alternate embodiments of the present invention, existing roadway markers or reflectors embedded in the roadway <NUM> either in the overall roadway centerline and/or the lane markers can also provide a contrasting thermal signature easily detected by the IR sensors <NUM>. In operation, such thermal markers would exhibit a thermal signature that will be different from the surrounding roadway <NUM>. A vehicle <NUM> can be aware of the general design of the roadway <NUM> via the navigation system so that the CAN Bus <NUM>, upon receipt of left and right edge data based on measurements of the appropriate markers, can accurately establish a centerline 112C for the roadway <NUM> or a particular travel lane <NUM>, and determine necessary adjustments accordingly.

The system <NUM> of the present invention is designed to be an adjunct to other sensors and guidance systems during times of challenging ambient conditions, and as such would enhance an autonomous vehicle's guidance system.

Unlike many optically-based guidance systems that have diminished effectiveness at night, especially in poorly light sections of the roadway <NUM>, the system <NUM> of the present invention functions with equal effectiveness day or night, regardless of lighting conditions.

Claim 1:
A computer-implemented method for centrally locating a vehicle (<NUM>) within a travel lane (<NUM>) on a roadway (<NUM>), said method comprising:
determining a left edge (<NUM>) of the roadway (<NUM>) using thermal imaging;
determining a right edge (112R) of the roadway (<NUM>) using thermal imaging;
determining a centerline (112C) of the travel lane (<NUM>) based on the determined left and right edges (<NUM>, 112R) of the roadway (<NUM>);
comparing the determined centerline (112C) of the travel lane (<NUM>) with the actual position of the vehicle (<NUM>); and
identifying any adjustment for the vehicle's position based on the comparison, characterized in that
determining each of the left edge (<NUM>) and the right edge (112R) of the roadway (<NUM>) comprises identifying, for each of the left edge (<NUM>) and the right edge (112R), a thermal difference between a first thermal signature representative of a portion of the roadway being imaged and a second thermal signature representative of a non-roadway portion (<NUM>) being imaged, the non-roadway portion being located proximate to the roadway portion.