Self-driving vehicle collision management system

A method generates and implements a real-time amelioration action for ameliorating an imminent collision between a self-driving vehicle (SDV) and an object. One or more processors detect that an imminent collision by a self-driving vehicle (SDV) is imminent with a confidence C1, and determine whether the SDV has an occupant of occupant type P with a confidence C2. One or more processors identify an object with which the imminent collision by the SDV is imminent with a confidence C3, and then generate and implement, based on C1, C2, C3, and P, a real-time amelioration action for ameliorating the imminent collision between the SDV and the object.

BACKGROUND

The present disclosure relates to the field of vehicles, and specifically to the field of self-driving vehicles. Still more specifically, the present disclosure relates to the field of managing self-driving vehicles during collision events.

Self-driving vehicles (SDVs) are vehicles that are able to autonomously drive themselves through private and/or public spaces. Using a system of sensors that detect the location and/or surroundings of the SDV, logic within or associated with the SDV controls the speed, propulsion, braking, and steering of the SDV based on the sensor-detected location and surroundings of the SDV.

SUMMARY

In an embodiment of the present invention, a method and/or computer program product generates and implements a real-time amelioration action for ameliorating an imminent collision between a self-driving vehicle (SDV) and an object. One or more processors detect that an imminent collision by a self-driving vehicle (SDV) is imminent with a first confidence, and determine whether the SDV has an occupant of a particular occupant type with a second confidence. One or more processors identify an object with which the imminent collision by the SDV is imminent with a third confidence, and then generate and implement, based on the first confidence, the second confidence, the third confidence, and the occupant type, a real-time amelioration action for ameliorating the imminent collision between the SDV and the object.

In an embodiment of the present invention, a system includes an on-board computer with one or more processors, one or more computer readable memories, one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories. The stored program instructions include: program instructions configured to detect that an imminent collision by a self-driving vehicle (SDV) is imminent with a first confidence; program instructions configured to determine whether the SDV has an occupant of a particular occupant type with a second confidence; program instructions configured to identify an object with which the imminent collision by the SDV is imminent with a third confidence; and program instructions configured to generate and implement, based on the first confidence, the second confidence, the third confidence, and the occupant type, a real-time amelioration action for ameliorating the imminent collision between the SDV and the object.

DETAILED DESCRIPTION

With reference now to the figures, and in particular toFIG. 1, there is depicted a block diagram of an exemplary system and network that may be utilized by and/or in the implementation of the present invention. Some or all of the exemplary architecture, including both depicted hardware and software, shown for and within computer101may be utilized by software deploying server149and/or other systems155shown inFIG. 1, and/or monitoring system201shown inFIG. 2, and/or a self-driving vehicle (SDV) on-board computer301shown inFIG. 3.

Exemplary computer101includes a processor103that is coupled to a system bus105. Processor103may utilize one or more processors, each of which has one or more processor cores. A video adapter107, which drives/supports a display109(which may be a touch screen capable of receiving touch inputs), is also coupled to system bus105. System bus105is coupled via a bus bridge111to an input/output (I/O) bus113. An I/O interface115is coupled to I/O bus113. I/O interface115affords communication with various I/O devices, including a keyboard117, a speaker119, a media tray121(which may include storage devices such as CD-ROM drives, multi-media interfaces, etc.), a transceiver123(capable of transmitting and/or receiving electronic communication signals), and external USB port(s)125. While the format of the ports connected to I/O interface115may be any known to those skilled in the art of computer architecture, in one embodiment some or all of these ports are universal serial bus (USB) ports.

As depicted, computer101is able to communicate with a software deploying server149and/or other systems155(e.g., establishing communication between monitoring system201and SDV202shown inFIG. 2) using a network interface129. Network interface129is a hardware network interface, such as a network interface card (NIC), etc. Network127may be an external network such as the Internet, or an internal network such as an Ethernet or a virtual private network (VPN). In one or more embodiments, network127is a wireless network, such as a Wi-Fi network, a cellular network, etc.

A hard drive interface131is also coupled to system bus105. Hard drive interface131interfaces with a hard drive133. In one embodiment, hard drive133populates a system memory135, which is also coupled to system bus105. System memory is defined as a lowest level of volatile memory in computer101. This volatile memory includes additional higher levels of volatile memory (not shown), including, but not limited to, cache memory, registers and buffers. Data that populates system memory135includes computer101's operating system (OS)137and application programs143.

OS137includes a shell139, for providing transparent user access to resources such as application programs143. Generally, shell139is a program that provides an interpreter and an interface between the user and the operating system. More specifically, shell139executes commands that are entered into a command line user interface or from a file. Thus, shell139, also called a command processor, is generally the highest level of the operating system software hierarchy and serves as a command interpreter. The shell provides a system prompt, interprets commands entered by keyboard, mouse, or other user input media, and sends the interpreted command(s) to the appropriate lower levels of the operating system (e.g., a kernel141) for processing. While shell139is a text-based, line-oriented user interface, the present invention will equally well support other user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS137also includes kernel141, which includes lower levels of functionality for OS137, including providing essential services required by other parts of OS137and application programs143, including memory management, process and task management, disk management, and mouse and keyboard management.

Application programs143include a renderer, shown in exemplary manner as a browser145. Browser145includes program modules and instructions enabling a world wide web (WWW) client (i.e., computer101) to send and receive network messages to the Internet using hypertext transfer protocol (HTTP) messaging, thus enabling communication with software deploying server149and other systems.

Application programs143in computer101's system memory (as well as software deploying server149's system memory) also include a Program for Controlling a Self-Driving Vehicle in Response to an Imminent Collision (PCSDVRIC)147. PCSDVRIC147includes code for implementing the processes described below, including those described inFIGS. 2-6. In one embodiment, computer101is able to download PCSDVRIC147from software deploying server149, including in an on-demand basis, wherein the code in PCSDVRIC147is not downloaded until needed for execution. In one embodiment of the present invention, software deploying server149performs all of the functions associated with the present invention (including execution of PCSDVRIC147), thus freeing computer101from having to use its own internal computing resources to execute PCSDVRIC147.

Also within computer101is a positioning system151, which determines a real-time current location of computer101(particularly when part of a self-driving vehicle as described herein). Positioning system151may be a combination of accelerometers, speedometers, etc., or it may be a global positioning system (GPS) that utilizes space-based satellites to provide triangulated signals used to determine two-dimensional or three-dimensional locations.

Also associated with computer101are sensors153, which detect an environment of the computer101and/or the state of occupants of a self-driving vehicle (SDV). More specifically, when detecting the environment of the SDV, sensors153are able to detect vehicles, road obstructions, pavement, etc. For example, if computer101is on board a self-driving vehicle (SDV), then sensors153may be cameras, radar transceivers, etc. that allow the SDV to detect the environment (e.g., other vehicles, road obstructions, pavement, etc.) of that SDV, thus enabling it to be autonomously self-driven. Similarly, sensors153may be cameras, thermometers, microphones (e.g., microphone331shown inFIG. 3), light sensors such as light sensor329shown inFIG. 3for detecting how dark a roadway is, chemical sensors such as chemical sensor327shown inFIG. 3for detecting chemical spills on a roadway, moisture detectors, etc. that detect ambient weather conditions, traffic conditions (as detected by the cameras, microphones, etc.), and other environmental conditions of a roadway upon which the SDV is traveling. When detecting the state of occupants of the SDV, sensors153may be any type of device capable of detecting the biometric state of the occupants of the SDV, including but not limited to cameras (that detect facial and body movements), microphones such as microphone331shown inFIG. 3that detect vocalizations, body sounds, etc. emanating from the occupants, biometric sensors (e.g., electrocardiograph (ECG/EKG) monitors, blood pressure monitors, etc.), etc.

As used herein, the term “occupant” is used to describe both animate occupants (i.e., passengers such as humans, pets, etc.) as well as inanimate occupants (i.e., cargo).

With reference now toFIG. 2, an exemplary self-driving vehicle (SDV)202is depicted traveling along a roadway204. Assume now that SDV202determines (e.g., using the SDV on-board computer301described below inFIG. 3) that a collision between SDV202and vehicle206(which may be another SDV or a non-SDV vehicle) and/or a human pedestrian and/or an animal (domestic or wild) and/or a fixed object (e.g., a guardrail, a tree, etc.) is imminent. This imminent collision may be determined by the SDV on-board computer301within SDV202and/or by a processor within a monitoring system201(analogous in architecture to computer101shown inFIG. 1).

The present invention adjusts how SDV202will react to the imminent collision according to 1) what types of occupants (if any) are riding in SDV202, and/or 2) the nature of the object that is about to be hit by SDV202. For example, if there are human passengers within SDV202, then the SDV on-board computer301(and/or the monitoring system201) will put their lives and safety above physical damage that may occur to the object about to be hit, as described in detail herein. Alternatively, if there are no passengers within SDV202(i.e., SDV202is an SDV cargo vehicle with no passengers), then SDV202can be violently stopped (in order to avoid a collision) with no harm to any person.

As described herein, the type of ameliorative action taken by SDV202may be influenced by signals from roadway sensor(s)208of a problem on roadway204. For example, roadway sensor(s)208may detect heavy traffic, ice on the road, windy conditions, a loose object on the roadway, etc. As such, SDV202may take ameliorative steps that take these conditions into account.

With reference now toFIG. 3, additional details of one or more embodiments of the SDV202are presented.

As shown inFIG. 3, SDV202has an SDV on-board computer301that controls operations of the SDV202. According to directives from a driving mode device307, the SDV202can be selectively operated in manual mode or autonomous mode. In a preferred embodiment, driving mode device307is a dedicated hardware device that selectively directs the SDV on-board computer301to operate the SDV202in one of the autonomous modes or in the manual mode.

While in autonomous mode, SDV202operates without the input of a human driver, such that the engine, steering mechanism, braking system, horn, signals, etc. are controlled by the SDV control processor303, which is now under the control of the SDV on-board computer301. That is, by the SDV on-board computer301processing inputs taken from navigation and control sensors309and the driving mode device307(indicating that the SDV202is to be controlled autonomously), then driver inputs to the SDV control processor303and/or SDV vehicular physical control mechanisms305are no longer needed.

As just mentioned, the SDV on-board computer301uses outputs from navigation and control sensors309to control the SDV202. Navigation and control sensors309include hardware sensors that 1) determine the location of the SDV202; 2) sense other cars and/or obstacles and/or physical structures around SDV202; 3) measure the speed and direction of the SDV202; and 4) provide any other inputs needed to safely control the movement of the SDV202.

With respect to the feature of 1) determining the location of the SDV202, this can be achieved through the use of a positioning system such as positioning system151shown inFIG. 1. Positioning system151may use a global positioning system (GPS), which uses space-based satellites that provide positioning signals that are triangulated by a GPS receiver to determine a 3-D geophysical position of the SDV202. Positioning system151may also use, either alone or in conjunction with a GPS system, physical movement sensors such as accelerometers (which measure acceleration of a vehicle in any direction), speedometers (which measure the instantaneous speed of a vehicle), airflow meters (which measure the flow of air around a vehicle), etc. Such physical movement sensors may incorporate the use of semiconductor strain gauges, electromechanical gauges that take readings from drivetrain rotations, barometric sensors, etc.

With respect to the feature of 2) sensing other cars and/or obstacles and/or physical structures around SDV202, the positioning system151may use radar or other electromagnetic energy that is emitted from an electromagnetic radiation transmitter (e.g., transceiver323shown inFIG. 3), bounced off a physical structure (e.g., another car), and then received by an electromagnetic radiation receiver (e.g., transceiver323). An exemplary positioning system within SDV202is a Light Detection and Ranging (LIDAR) (e.g., LIDAR333shown inFIG. 3) or Laser Detection and Ranging (LADAR) system that measures the time it takes to receive back the emitted electromagnetic radiation (e.g., light), and/or evaluate a Doppler shift (i.e., a change in frequency to the electromagnetic radiation that is caused by the relative movement of the SDV202to objects being interrogated by the electromagnetic radiation) in the received electromagnetic radiation from when it was transmitted, the presence and location of other physical objects can be ascertained by the SDV on-board computer301.

With respect to the feature of 3) measuring the speed and direction of the SDV202, this can be accomplished by taking readings from an on-board speedometer (not depicted) on the SDV202and/or detecting movements to the steering mechanism (also not depicted) on the SDV202and/or the positioning system151discussed above.

With respect to the feature of 4) providing any other inputs needed to safely control the movement of the SDV202, such inputs include, but are not limited to, control signals to activate a horn, turning indicators, flashing emergency lights, etc. on the SDV202.

In one or more embodiments of the present invention, SDV202includes roadway sensors311that are coupled to the SDV202. Roadway sensors311may include sensors that are able to detect the amount of water, snow, ice, etc. on the roadway204(e.g., using cameras, heat sensors, moisture sensors, thermometers, etc.). Roadway sensors311also include sensors that are able to detect “rough” roadways (e.g., roadways having potholes, poorly maintained pavement, no paving, etc.) using cameras, vibration sensors, etc. Roadway sensors311may also include sensors that are also able to detect how dark the roadway204is using light sensors.

Similarly, a dedicated camera321can be trained on roadway204, in order to provide photographic images of conditions on the roadway204upon which the SDV202is traveling.

Similarly, a dedicated object motion detector319(e.g., a radar transceiver capable of detecting Doppler shifts indicative of the speed and direction of movement of other vehicles, animals, persons, etc. on the roadway204) can be trained on the roadway204upon which the SDV202is traveling.

In one or more embodiments of the present invention, also within the SDV202are SDV equipment sensors315. SDV equipment sensors315may include cameras aimed at tires on the SDV202to detect how much tread is left on the tire. SDV equipment sensors315may include electronic sensors that detect how much padding is left of brake calipers on disk brakes. SDV equipment sensors315may include drivetrain sensors that detect operating conditions within an engine (e.g., power, speed, revolutions per minute—RPMs of the engine, timing, cylinder compression, coolant levels, engine temperature, oil pressure, etc.), the transmission (e.g., transmission fluid level, conditions of the clutch, gears, etc.), etc. SDV equipment sensors315may include sensors that detect the condition of other components of the SDV202, including lights (e.g., using circuitry that detects if a bulb is broken), wipers (e.g., using circuitry that detects a faulty wiper blade, wiper motor, etc.), etc. Thus, in one or more embodiments of the present invention, if the SDV performs an ameliorative maneuver (e.g., pulls off to the shoulder210of roadway204, collides with an object with the crumple zone402aof the SDV202, etc.), the SDV will warns the occupants before pulling off to the shoulder210, such that this anomalous maneuver will not surprise the occupants of the SDV (particularly due to equipment malfunctions on the SDV and/or roadway conditions).

In one or more embodiments of the present invention, also within SDV202is a communications transceiver317, which is able to receive and transmit electronic communication signals (e.g., RF messages) from and to other communications transceivers found in other vehicles, servers, monitoring systems, etc.

In one or more embodiments of the present invention, also within SDV202is a telecommunication device325(e.g., a smart phone, a cell phone, a laptop computer, etc.), which may be connected (e.g., via a near field communication—NFC connection) to the SDV on-board computer301.

In one or more embodiments of the present invention, also within SDV202is a speaker337, which is able to broadcast aural warnings (e.g., a buzzer, alarm, or computer-generated voice) that apprise the occupants of the SDV202and/or other persons/vehicles of an upcoming ameliorative maneuver SDV202will be performing.

In one or more embodiments of the present invention, also within SDV202is a video display339, which is able to display visual warnings (e.g., a flashing light, a text message, etc.) that apprise the occupants of the SDV202and/or other persons/vehicles of an upcoming ameliorative maneuver SDV202will be performing.

In one or more embodiments of the present invention, also within SDV202is a proximity sensor341, which uses motion detectors, radar (using Doppler shifting logic), etc. that detect an object (e.g., a vehicle in a next lane) near SDV202.

In one or more embodiments of the present invention, also within SDV202is a tire rupturing system343, which is capable of causing one or more tires on SDV202to blow out. For example, tire rupturing system343may be an explosive device (e.g., compressed air canister) that, when activated by SDV on-board computer301, will cause a tire to blow out, resulting in SDV202coming to an abrupt slow-down.

Self-driving vehicles (SDVs) are likely to be ubiquitous on public roadways at some point in time. Despite the expected superiority of SDVs over human drivers (due to the vastly quicker response times of computers that control SDVs over human drivers that control non-SDV vehicles), some SDV collisions may be unavoidable.

In accordance with one or more embodiments of the present invention, if an SDV has no occupant, then the SDV may take certain actions that may lead to its own demise so as to avoid harm to an object about to be struck (e.g. another car with passengers, a pedestrian, etc.). That is, if the SDV “knows” that it has no human passengers, and the SDV is about to hit another vehicle that may or may not contain human passengers, then the SDV will sacrifice its own well being (e.g., by swerving off a cliff) rather than hitting the other vehicle.

Thus and as described herein, the present invention utilizes a self-driving vehicle (SDV), a means for determining that a crash (collision) is imminent with confidence C1, a means for determining if the SDV has a passenger (occupant) of type P with confidence C2, and a means for determining aspects of the object to be collided with, with confidence C3. Based, on C1, C2, C3, and P, the system plans a real-time amelioration action. For example, if the SDV has no occupant, it may take certain actions that may cause more damage to itself in a collision (such as breaking apart more easily) than it would if it had an occupant.

The determining of a crash with confidence C1 may be based on analytics of sensor data (e.g., a captured visual image of an object in a travel path, LIDAR information about a distance between the SDV202and another vehicle/object, etc.). That is, the determination that SDV202is about to be involved in a collision with another object is at a confidence level C1. For example, C1 may be “there is a 90% probability that SDV202is about to hit an object if no ameliorative steps are taken to avoid that object”.

Passenger type P may be any of human, pet, package (e.g. for delivery), or no passenger at all. Confidence C2 is the confidence level (e.g., probability) that the system has accurately identified what type of occupant (animate or inanimate) is currently within SDV202.

Confidence C3 is the confidence level (e.g., probability) that the system has accurately identified what type of object is about to be hit by SDV202(i.e., is the object a manually controlled vehicle, an SDV, a pedestrian, an animal, etc.), and/or what types and quantity of occupants are within the other vehicle (assuming that the object that the SDV202is about to hit is another vehicle).

As described herein, the object that SDV202is about to collide with may be any of: another SDV (with or without passenger), another vehicle that is not an SDV, a person, an animal, a tree, a rock, a guard rail, a deer, school bus, bridge, etc.

In addition, the object may be behind SDV202. For example, when an SDV senses it is about to become disabled on a road with fast moving traffic close behind, and when a shoulder is narrow or unavailable, the SDV without passengers may determine that driving over an embankment poses less risk to human drivers behind it than becoming disabled in the roadway.

In various embodiments of the present invention, the amelioration action may be one or more of: allowing the SDV202to break apart (so as to lesson the impact on the object to be collided with); performing a certain kind of very aggressive braking or steering maneuver; allowing the SDV202to self-destruct (e.g., with a tree on the side of the road); blowing out the SDV tires (in order to quickly bring the SDV202to a stop); not triggering the air bags within the SDV202(if no occupant is in the car); allowing the SDV202to “crumple” into the occupant area if there is no occupant; etc.

With reference now toFIG. 4, crumple zones402a-404bon SDV202are designed to absorb the energy from the impact during a traffic collision by controlled deformation. That is, SDV202is designed such that crumple zones402a-404bwill absorb the energy of a collision, thereby protecting the passengers riding in the passenger section404. Crumple zones402a-404bmay use aluminum, composite/carbon fiber honeycomb, or energy absorbing foam to form an impact attenuator that dissipates crash energy. Thus, if the SDV on-board computer301within SDV202knows the energy absorbing capabilities of crumple zone402aand/or crumple zone402b, and that there are passengers riding within passenger section404, then the SDV on-board computer301within SDV202will maneuver SDV202just before a collision such that the collision energy is absorbed by crumple zone402aand/or crumple zone402b, thereby protecting the passengers within passenger section404.

The object characterization (of the object to be collided with) may include an assessment of object's weight (since the results of collision may depend on the relative weight of the SDV202and the object being struck).

If the SDV202has no passenger, then SDV202need not take passenger “whiplash” into account when taking actions, nor the excessive “G” forces occurring with very aggressive braking, swerving, etc.

The present invention is able to handle many collision scenarios. For example, suppose that SDV202“knows” it will crash (e.g., based on sensor readings from sensors on SDV202) head-on into another vehicle or a fixed object (e.g., a large appliance that just fell off the back of a truck in front of the SDV202). If SDV202has a human passenger, it might swerve to hit another vehicle moving in the same direction, thus relying on the crumple zones of both vehicles to protect their passengers (including the passengers within SDV202). However, if SDV202has no passenger, then it would be willing sacrifice itself by breaking apart, slamming into a concrete wall, driving over a cliff, etc., thereby protecting passengers in other vehicles (about to be hit by SDV202) from harm. In addition, pile-ups on highways remain a very serious danger to drivers, sometimes involving more than 100 cars. When an SDV such as SDV202is involved in a pileup and has no passengers, the present invention enables SDV202to change vehicle parameters in order to absorb more of the pileup as the pileup grows in size, even to the point of SDV202allowing itself to be pulverized (in order to provide an additional barrier/cushion to other vehicles).

In accordance with one or more embodiments of the present invention, an electronic system (e.g., SDV on-board computer301) in SDV202includes crash prediction modules and sensor systems each arranged to sense or predict an imminent crash involving SDV202. An occupant-sensing system may detect the presence of an occupant. The crash prediction system(s) and the occupant system(s) may be connected to a bus and supplied with power by the bus and communication through the bus. Each occupant device and crash-prediction device may be actuated in the event of a predicted crash involving the vehicle as sensed by a sensor system. The system for predicting and evading crashes of the SDV may include an imaging system of the surroundings.

In one or more embodiments of the present invention, SDV202has an array of sensors, which are employed to detect an imminent collision. For example, consider SDV202as depicted inFIG. 5.

A LIDAR system533(analogous to LIDAR333depicted inFIG. 3) uses a rotating roof top camera, which acts a laser range finder. With its array of multiple (e.g., 64 or more) laser beams, this camera creates 3-D images of objects, which helps the car register/see hazards along the way. This device calculates how far an object is from the moving vehicle based on the time it takes for the laser beams to hit the object and come back. These high intensity lasers can calculate distance and create images for objects in a 200 meter range.

Distance sensors519(analogous to object motion detector319shown inFIG. 3) may use radar transceivers mounted on the front and rear bumpers of SDV202to enable SDV202to be aware of vehicles in front of it and behind it. As known to those skilled in the art of electronics, radar is an object-detection system that uses radio waves to determine the range, angle, or velocity of objects.

As depicted inFIG. 5, a video camera521(analogous to camera321shown inFIG. 3) may be mounted on the windshield of SDV202. This camera, with the help of image processing and artificial intelligence found within on-board SDV computer301, will interpret common road behavior and motorist signs. For example, if a cyclist gestures that he intends to make a turn, the driverless car interprets it correctly and slows down to allow the cyclist to turn. Predetermined shape and motion descriptors are programmed into the system to help the SDV202make intelligent decisions.

Position estimator541(analogous to proximity sensor341shown inFIG. 3) may be configured as an ultrasonic sensor, which uses sound propagation to detect objects. Position estimator541can also be used as a geophysical location device, when mounted on one of the rear wheels of SDV202, thus enabling position estimator541to calculate the number of rotations of the wheel in order to find the exact location of the SDV202.

In one or more embodiments of the present invention, crash prediction is achieved through the user of a neural network (e.g., part of SDV on-board computer301) which has been previously trained with training data to predict the possibility of crashing, where the training data represents ever-changing views previously picked-up by an image picking-up means during the driving of vehicles.

The SDV202may include a vehicle travel management system that monitors the location of vehicles in a travel lane, as well as other objects in the vicinity of the SDV202. This information is then used by the SDV on-board computer to generate an amelioration action upon determining that SDV202is about to be involved in a collision.

In one or more embodiments, the present invention employs a pre-crash system with a controller coupled to object sensors, which have a vision system that includes image sensors to detect an impending impact (including side impacts). The image sensors provide a high frame rate to a video-processing chip, which in turn is coupled to a general purpose controller. The general-purpose controller may determine whether an object is an impending threat and controls the deployment of an airbag through an airbag controller or other counter-measure. If the SDV has no occupants, the airbag need not deploy, and the SDV may allow greater destruction to its body, since no human is in the SDV. The high frame rate used in the invention allows not only the determination of distance to the object but the velocity of the object as well as the acceleration of the object, and these findings are factored into the deployment decision.

In one or more embodiments, the vehicle navigation and crash assignment task is formulated as a constrained optimal control problem. A predictive, model-based controller iteratively plans an optimal or best-case vehicle trajectory through a constrained corridor. This best-case scenario is used to establish the minimum threat posed to the vehicle given its current state, current and past SDV inputs/performance, and environmental conditions. Based on this threat assessment, the level of controller intervention required to control collisions or instability is calculated. Various vehicle models, actuation modes, trajectory-planning objectives, etc. can be seamlessly integrated without changing the underlying controller structure. As an example, if the SDV is about to be hit from the rear (rear-ended), the SDV might release its brakes to reduce the damage to the vehicle behind it. Alternatively, the SDV that is about to be hit, could turn its wheels so that it doesn't collide into another vehicle when it is rear-ended.

In one or more embodiments of the present invention, an SDV safety system continuously evaluates data relating to the surroundings from a surroundings sensor system in terms of an imminent collision with an obstacle, and autonomous emergency braking is initiated in the event of an unavoidable collision. However, as described herein, this braking may be of a different nature if the SDV is unoccupied, since there is no danger to a possible occupant in the SDV from the high “G” forces experienced in aggressive braking.

In one or more embodiments, the present invention makes use of a guidance system through on-board GPS and two-way communication hardware. The current position of the vehicle may be continuously monitored and correlated to the position of potential hazards along its path, so that corrective action can be taken by implementing appropriate, predetermined control strategies. In various embodiments, the SDV will take into account its own operating tolerances.

While determining the amelioration action to take to either reduce the likelihood of a collision or to take a collision path that would reduce the likelihood of injuring a human, the SDV will also analyze the potential secondary impacts of its set of possible actions. For example, if a potential amelioration action is to break apart to avoid colliding with an object in front of it, the SDV will also determine whether breaking apart would potentially cause another collision with objects, vehicles, people or animals in the vicinity (potentially in front, on the sides or behind it). The SDV will then choose the optimal amelioration action with both the consideration of the potential initial collision against the potential impacts from selection of a specific action.

The SDV may also take into account financial considerations in the determination of the action. For example, the SDV may determine that there is an object obstructing the road (e.g., a tree branch) and that a collision could be entirely avoided if the SDV were to break apart. However, such breaking apart would cause a significant financial loss (the value of the SDV). An alternate option would be to allow the collision to occur, thus incurring a much lower financial loss (limited to the damaged areas of the SDV and required repairs) and/or the inanimate property it would collide with. The financial value assessment and loss determination would only be used to weight the actions in cases where a financial value can be definitively identified and not in cases where human or animal life is involved.

While the present invention has been described using scenarios in which the object about to be hit the by SDV are other vehicles, fixed objects, etc., the present invention is also useful when applied to scenarios in which the SDV is about to collide with a very heavy moving vehicle, such as a railroad train. The SDV may take differential actions based on whether the SDV has an occupant or is empty. That is, if the SDV has no passengers, and if swerving to miss a train endangers other persons, and the SDV is significantly lighter than the oncoming train, then the SDV may sacrifice itself by letting the train hit it, rather than maneuvering in a manner that endangers pedestrians, other small vehicles, etc.

In one or more embodiments, the present invention takes into account the passable width of a road, the width of available shoulder, as well as the threat posed by going beyond passable road (e.g., colliding with cars going in opposite direction, or falling off downhill lane of switchback road up steep hill) when generating an ameliorative action. The SDV may have knowledge of a road map and forecast traffic/weather, even if it cannot see 100% with its real-time sensors.

SDVs may have physical areas that are more hardened to withstand accidents than other areas on its physical body, such as if two or more SDVs decide to collide with each other to achieve optimal ethical outcome, they may align themselves to collide on their respective hardened areas in a best effort manner. If an SDV has decided to sacrifice itself, then it may decide not to collide on the hardened areas. Also, certain regions may have more shock-absorbing features than others, and this may be taken into account.

In one or more embodiments of the present invention, the SDV may immediately move the seating within the car in order to minimize injuries to the passenger(s). That is, the SDV on-board computer301may push back a front seat in the passenger compartment of the SDV just before the collision occurs, thereby giving the passengers more room between themselves and deploying air bags.

In one or more embodiments of the present invention, SDV202can also learn from other SDVs around the same geolocation and context (e.g. road conditions/weather) regarding what decision they have taken and the outcomes of those decisions a priori and apply such lessons when similar situations arise. That is, the SDV on-board computer301in SDV202can use a probabilistic function to define the best possible option based on the other SDVs and human inputs.

In one or more embodiments of the present invention, a human driver or passenger may be allowed to override the decision of the SDV202if time permits.

In an embodiment of the present invention in which SDV202is unoccupied, a full-size interior airbag may deploy if the SDV senses that it is about to be in an accident to minimize interior damage. Alternatively, the interior may fill with a fast expanding foam (to protect the interior of the SDV202) and/or with fire-retarding agents and foams to inhibit explosions and fires. Such actions are not taken if the SDV202has people in it.

With reference now toFIG. 6, a high-level flow chart illustrating a process performed by one or more processors and/or other hardware devices in accordance with one or more embodiments of the present invention is presented.

After initiator block602, one or more processors (e.g., within SDV on-board computer301shown inFIG. 3) detect that an imminent collision by a self-driving vehicle (SDV) is imminent with a confidence C1, as described in block604. For example, SDV on-board computer301within SDV202may determine that SDV202is about to collide with another vehicle, fixed object, pedestrian, etc., unless ameliorative steps are taken to alter the path of SDV202. This determination has a confidence level C1, which is the probability (e.g., 95%) that the SDV on-board computer301has accurately made the prediction. This confidence level C1 may be based on past experiences with other SDV on-board computers301programmed in a similar manner (and/or under similar roadway conditions, traffic conditions, SDV configuration, etc.). That is, if other SDV on-board computers301have correctly predicted that the SDVs in which they are located are going to have an immediate collision 95% of the time, then confidence level C1 would be 95.

As described in block606, one or more processors determine whether the SDV has an occupant of occupant type P with a confidence C2. This confidence level C2 may be based on past experiences with other SDV on-board computers301programmed in a similar manner and/or utilizing similar sensors within the cabin of SDV202(e.g., camera321aimed at the interior of the cabin of SDV202). That is, if other SDV on-board computers301have correctly identified the occupant type P (i.e., where P=human) 99% of the time, then confidence level C2 would be 99.

As described in block608, one or more processors identify an object with which the imminent collision by the SDV is imminent with a confidence C3. For example, the processors may identify vehicle206in front of SDV202shown inFIG. 2with a 90% confidence level. This confidence level C3 may be based on past experiences with the SDV on-board computer301within SDV202and/or other SDV on-board computers301on other SDVs that have been programmed in a similar manner (and/or under similar roadway conditions, traffic conditions, SDV configuration, etc.). That is, if these SDV on-board computers301have correctly identified objects that are about to be hit by the SDV in which they are located are going to have an immediate collision 90% of the time, then confidence level C3 may be 90.

Thus, confidence level C1 reflects how confident the system is that it has correctly predicted/detected an imminent collision. Confidence level C2 reflects how confident the system is that is has corrected determined what type of occupants (if any) are currently in the SDV. Confidence level C3 reflects how confident the system is that it has correctly identified the object about to be hit by the SDV. Confidence level C3 is based on 1) how confident the system is that it has detected the object about to be hit by the SDV, and/or 2) how confident the system is that it has identified what type of object (person, vehicle, animal, etc.) is about to be hit.

As described in block610, one or more processors then generate and implement, based on C1, C2, C3, and P, a real-time amelioration action for ameliorating the imminent collision between the SDV and the object. That is, once values for C1, C2, C3 and P are entered into the SDV on-board computer301, then SDV on-board computer301is able to tell the SDV202what ameliorative steps (braking, speeding up, swerving, crashing into another object, etc.) the SDV202is to take.

The flow chart ends at terminator block612.

In a further embodiment of the present invention, one or more processors determine confidence C1 based on an analysis of sensor data received in real-time from one or more sensors on the SDV. That is, SDV on-board computer301is able to determine how confident it is that it has correctly identified the object and/or object type about to be hit by SDV202based on camera readings (e.g., from vehicle camera521), LIDAR533, microphone331(detecting the sound of the object about to be hit), etc.

In an embodiment of the present invention, the occupant type P describes animate passengers in the SDV. That is, the system (e.g., using biometric sensor335and/or camera321and/or microphone331trained on passengers in SDV202) will provide sensor readings that are associated with animate passengers. For example, such sensors may detect a barking sound in the cabin on SDV202, indicative of a dog inside the SDV202. SDV on-board computer301will then adjust the real-time ameliorative action accordingly.

In an embodiment of the present invention, the occupant type describes inanimate passengers in the SDV. For example, if biometric sensor335and/or camera321and/or microphone331trained on an interior cabin of SDV202detect no life forms, then SDV on-board computer301will assume that SDV202is only hauling cargo, and will adjust the real-time ameliorative action accordingly. That is, if no human lives are in danger, the real-time ameliorative action will be more likely to result in dramatic braking, deliberately hitting a wall, etc., unless doing so would damage the cargo.

In an embodiment of the present invention, the occupant type describes a lack of any occupants in the SDV. For example, if camera321trained on an interior cabin of SDV202detects no life forms, no cargo, etc., then SDV on-board computer301will assume that SDV202is empty, and will adjust the real-time ameliorative action accordingly. That is, if there is no risk to either human passengers or cargo, the real-time ameliorative action will be more likely to result in drastic steps up to and including severely damaging the SDV202in order to avoid risking damage/injury to other entities.

In an embodiment of the present invention, the object to be imminently collided with by the SDV is another SDV that has a human passenger. As such, the SDV on-board computer301in SDV202will generate a real-time ameliorative action that poses the least risk to both passengers in SDV202(if any) as well as passengers in the other vehicle (e.g., vehicle206shown inFIG. 2) that is about to be hit.

In an embodiment of the present invention, the object to be imminently collided with by the SDV is another SDV that has no passenger. As such, the SDV on-board computer301in SDV202will generate a real-time ameliorative action that poses the least risk to passengers in SDV202, while not being concerned about property damage to the other vehicle (e.g., vehicle206shown inFIG. 2) that is about to be hit.

In an embodiment of the present invention, the object to be imminently collided with by the SDV is a pedestrian. As such, the SDV on-board computer will generate a real-time ameliorative action that poses the least risk to the pedestrian, even if that poses an elevated risk of injury to passengers in SDV202and damage to SDV202, since hitting the pedestrian would most likely cause grave bodily injury to the pedestrian.

In an embodiment of the present invention, the object to be imminently collided with by the SDV is a non-human animal. As such, the SDV on-board computer will generate a real-time ameliorative action that does not pose an undue risk to passengers in SDV202, nearby pedestrians, nearby vehicles when avoiding the non-human animal (e.g., a deer).

In an embodiment of the present invention, the object to be imminently collided with by the SDV is a vehicle that is not an SDV. That is, assume that SDV202is about to hit vehicle206shown inFIG. 2. Assume now that all (or at least most) SDVs broadcast a signal indicating that 1) they are in autonomous mode, and 2) are able to coordinate movements with other SDVs. As such, if vehicle206is another SDV, then SDV202and vehicle206can coordinate their movements in order to avoid hitting one another. However, if vehicle206is not an SDV, then the SDV on-board computer301in SDV202assumes that the course of movement of vehicle206will not change quickly enough to avoid the collision, and thus, SDV202may have to take all ameliorative steps to minimize the effect of the collision (if not avoiding it entirely).

In an embodiment of the present invention, the object to be imminently collided with the SDV is an inanimate object located in a fixed position (e.g., a tree branch in the middle of the road). As such, the SDV on-board computer301knows that this object will not be moving, and generates an ameliorative action that takes this into account (including, if necessary and being the safest alternative, simply allowing SDV202to hit the fixed object).

In an embodiment of the present invention, the amelioration action is to strike the object in a manner that causes energy-absorbing areas on the SDV to absorb an impact of the SDV striking the object. For example, if SDV202realizes that it cannot avoid hitting an object, it at least will position itself such that the crumple zone402atakes the main brunt of the collision, thereby protecting the passengers within the passenger section404of the SDV202shown inFIG. 4.

In an embodiment of the present invention, the amelioration action is to cause at least one tire on the SDV to burst. For example, assume that SDV202is unoccupied. Assume further that SDV202is equipped with a tire rupturing system (e.g., tire rupturing system343shown inFIG. 3). If SDV on-board computer301directs the tire rupturing system343to cause one or more tires on the SDV202to blow out (rupture), this will result in SDV202abruptly slowing down (due to the drag and resistance caused by the ruptured tires).

In an embodiment of the present invention, assume that the SDV is unoccupied. If so, then the amelioration action in this embodiment is to prevent any airbags within the SDV from deploying in response to the SDV colliding with the object. That is, deploying airbags may damage cargo within the SDV, or may simply provide no benefit since there are no passengers (while still incurring the cost of replacement of the deployed airbags). As such, the airbags will be disabled. Note that there still may be a pressure sensor on seats in the SDV. However, the camera will detect that this pressure comes from cargo, not passengers, and will then disable the airbags.

In an embodiment of the present invention, one or more processors determine a weight ratio between the SDV and the object, and then adjust the amelioration action according to the weight ratio between the SDV and the object. For example, assume that SDV202is passenger sedan (with passengers on board) that is about to be hit by a fast-moving train (e.g., a train that is going over 60 miles per hour). As such, SDV202will take any reasonable step, including hitting another vehicle, hitting a wall, etc., rather than being hit by the fast-moving train, which would certainly be fatal to the occupants of SDV202.

In an embodiment of the present invention, one or more processors adjust the amelioration action based on roadway conditions for a roadway on which the SDV is traveling. For example, if chemical sensors327inFIG. 3detect the presence of flammable liquids on roadway204shown inFIG. 2, then the SDV on-board computer301may devise an ameliorative action that avoids driving through the flammable liquids. Similarly, if SDV on-board computer301has been alerted of inclement weather on the roadway204and/or knows (e.g., from a loaded digital map within the SDV on-board computer301) that roadway204is about to curve sharply within 100 feet, then SDV on-board computer301will tailor the ameliorative action in order to accommodate these conditions.

In an embodiment of the present invention, assume that SDV202is a first SDV. One or more processors receive executable instructions for implementing an amelioration action performed by a group of other SDVs that experienced an imminent collision that was similar to the imminent collision being experienced by the first SDV, and then execute the executable instructions for implementing the amelioration action performed by the group of other SDVs. That is, SDV202can use ameliorative actions that were taken by other SDVs. Such ameliorative actions can be stored within the SDV on-board computer301within SDV202.

The present invention may be implemented in one or more embodiments using cloud computing. Nonetheless, it is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Characteristics are as Follows:

Deployment Models are as Follows: