Patent Description:
Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.

Motion planning and control are critical operations in autonomous driving. During a collision of an ADV and an obstacle, it is important to reduce damage to the ADV and the obstacle, and avoid to introduce damages to other obstacles. However, currently, the ADV is configured to keep a brake during the collision, which may not be always the best solution. For example, if the ADV is hit from behind, the ADV may not be able to absorb the energy from the collision if the ADV just keeps the brake.

<CIT> discloses a method for operating a distance control system for a vehicle F, in which a distance control of the vehicle F is performed on the basis of a distance D to a vehicle Fvor ahead on the same lane. The proposed method is characterized in that the target distance D is increased when a vehicle Frück approaching the vehicle F from behind on the same lane is recognized and based on the dynamics of the vehicles F and Frück, a rear-end collision between vehicle F and vehicle Frück is predicted at time tKoll.

<CIT> discloses a speed control device. It is determined that whether a rapid brake or a gentle brake is performed on a moving body based on a state of a second object, so that not only a collision with a first object but also a collision with the second object is avoided.

<CIT> discloses a control unit, which sets a front-end collision risk of a subject vehicle against a front vehicle in accordance with a time headway of the subject vehicle and a margin time to front-end collision of the subject vehicle, and a rear-end collision risk of the subject vehicle by a rear vehicle in accordance with a time headway of the rear vehicle and a margin time to rear-end collision of the subject vehicle, the margin time to rear-end collision having a larger weight than that of the margin time to front-end collision in the front-end collision risk against the front vehicle. Brake control and alarm control are performed in accordance with the front-end collision risk against the front vehicle and the rear-end collision risk by the rear vehicle.

<CIT> discloses a front impact mitigation system for a host vehicle and a method for operating a front impact mitigation system. The front impact mitigation system can take into account the position of a rear object that trails the host vehicle to develop a modified front impact mitigation control signal that at least partially mitigates the likelihood of certain rear impact collisions between the rear object and the host vehicle when the host vehicle is responding to the presence of an impending leading obstacle. A modified front impact mitigation control signal may be developed to account for the speed of the host vehicle and the distance that the rear object trails the host vehicle.

In a first aspect, a computer-implemented method for operating an autonomous driving vehicle (ADV) is provided as set out in claim <NUM>.

In a second aspect, a non-transitory machine-readable medium having instructions stored therein is provided as set out in claim <NUM>.

In a third aspect, a data processing system is provided as set out in claim <NUM>.

Various embodiments and aspects of the invention will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention, which is defined by the appended claims. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present invention.

According to some embodiments, a collision condition is determined by an ADV, for example, an inertial measurement unit (IMU) may monitor sudden changes in acceleration (> <NUM>). A current moving direction of the ADV and a closest obstacle in this moving direction may be determined. A gear position of the ADV may be set to a "neutral" position. A time duration to a second collision to the closest obstacle may be determined. If the time duration to the second collision is more than a predetermined time threshold, for example, <NUM> seconds, a deceleration may not be needed. Thus, a minimum deceleration may be determined to be zero. Accordingly, a brake command of "zero" may be generated. The ADV may not need to brake. If the time duration to the second collision is less than or equal to the predetermined time threshold, a minimum deceleration required to avoid the second collision may be calculated. For example, the minimum deceleration may be calculated based on a difference between a speed of the ADV and a speed of the second obstacle, and a distance between the ADV and the second obstacle. Then, the deceleration command may be applied to the ADV.

According to one embodiment, a first obstacle colliding with the ADV is detected. A minimum deceleration that is required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of a moving direction is determined. A brake command is generated based on the minimum deceleration. Then, the brake command is applied to the ADV, such that the ADV avoids collision with the second obstacle and softens an impact of the collision with the first obstacle.

In one embodiment, the first obstacle colliding with the ADV is detected based on detecting that an acceleration or deceleration of the ADV is larger than a predetermined acceleration or deceleration threshold. In one embodiment, a closest obstacle within the predetermined proximity of the moving direction is determined, where the second obstacle is the closest obstacle. In one embodiment, a gear position of the ADV is selected to be a "Neutral" position.

In one embodiment, whether a time duration when the ADV is to be in collision with the second obstacle is more than a predetermined time threshold is determined.

In one embodiment, in response to determining that the time duration when the ADV is to be in collision with the second obstacle is more than the predetermined time threshold, the minimum deceleration is determined to be zero, and the brake command is the brake command is a brake command of "zero". The ADV is allowed to slide to soften an impact of the collision with the first obstacle.

In one embodiment, in response to determining that the time duration when the ADV is to be in collision with the second obstacle is less than or equal to the predetermined time threshold, the minimum deceleration is calculated based on a difference between a speed of the ADV and a speed of the second obstacle, and a distance between the ADV and the second obstacle.

<FIG> is a block diagram illustrating an autonomous vehicle network configuration according to one embodiment of the invention. Referring to <FIG>, network configuration <NUM> includes autonomous vehicle <NUM> that may be communicatively coupled to one or more servers <NUM>-<NUM> over a network <NUM>. Although there is one autonomous vehicle shown, multiple autonomous vehicles can be coupled to each other and/or coupled to servers <NUM>-<NUM> over network <NUM>. Network <NUM> may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof, wired or wireless. Server(s) <NUM>-<NUM> may be any kind of servers or a cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. Servers <NUM>-<NUM> may be data analytics servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc..

An autonomous vehicle refers to a vehicle that can be configured to in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such an autonomous vehicle can include a sensor system having one or more sensors that are configured to detect information about the environment in which the vehicle operates. The vehicle and its associated controller(s) use the detected information to navigate through the environment. Autonomous vehicle <NUM> can operate in a manual mode, a full autonomous mode, or a partial autonomous mode.

In one embodiment, autonomous vehicle <NUM> includes, but is not limited to, perception and planning system <NUM>, vehicle control system <NUM>, wireless communication system <NUM>, user interface system <NUM>, and sensor system <NUM>. Autonomous vehicle <NUM> may further include certain common components included in ordinary vehicles, such as, an engine, wheels, steering wheel, transmission, etc., which may be controlled by vehicle control system <NUM> and/or perception and planning system <NUM> using a variety of communication signals and/or commands, such as, for example, acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc..

Components <NUM>-<NUM> may be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, components <NUM>-<NUM> may be communicatively coupled to each other via a controller area network (CAN) bus. A CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer. It is a message-based protocol, designed originally for multiplex electrical wiring within automobiles, but is also used in many other contexts.

Referring now to <FIG>, in one embodiment, sensor system <NUM> includes, but it is not limited to, one or more cameras <NUM>, global positioning system (GPS) unit <NUM>, inertial measurement unit (IMU) <NUM>, radar unit <NUM>, and a light detection and range (LIDAR) unit <NUM>. GPS system <NUM> may include a transceiver operable to provide information regarding the position of the autonomous vehicle. IMU unit <NUM> may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. Radar unit <NUM> may represent a system that utilizes radio signals to sense objects within the local environment of the autonomous vehicle. In some embodiments, in addition to sensing objects, radar unit <NUM> may additionally sense the speed and/or heading of the objects. LIDAR unit <NUM> may sense objects in the environment in which the autonomous vehicle is located using lasers. LIDAR unit <NUM> could include one or more laser sources, a laser scanner, and one or more detectors, among other system components. Cameras <NUM> may include one or more devices to capture images of the environment surrounding the autonomous vehicle. Cameras <NUM> may be still cameras and/or video cameras. A camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting a platform.

Sensor system <NUM> may further include other sensors, such as, a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone). An audio sensor may be configured to capture sound from the environment surrounding the autonomous vehicle. A steering sensor may be configured to sense the steering angle of a steering wheel, wheels of the vehicle, or a combination thereof. A throttle sensor and a braking sensor sense the throttle position and braking position of the vehicle, respectively. In some situations, a throttle sensor and a braking sensor may be integrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system <NUM> includes, but is not limited to, steering unit <NUM>, throttle unit <NUM> (also referred to as an acceleration unit), and braking unit <NUM>. Steering unit <NUM> is to adjust the direction or heading of the vehicle. Throttle unit <NUM> is to control the speed of the motor or engine that in turn controls the speed and acceleration of the vehicle. Braking unit <NUM> is to decelerate the vehicle by providing friction to slow the wheels or tires of the vehicle. Note that the components as shown in <FIG> may be implemented in hardware, software, or a combination thereof.

Referring back to <FIG>, wireless communication system <NUM> is to allow communication between autonomous vehicle <NUM> and external systems, such as devices, sensors, other vehicles, etc. For example, wireless communication system <NUM> can wirelessly communicate with one or more devices directly or via a communication network, such as servers <NUM>-<NUM> over network <NUM>. Wireless communication system <NUM> can use any cellular communication network or a wireless local area network (WLAN), e.g., using WiFi to communicate with another component or system. Wireless communication system <NUM> could communicate directly with a device (e.g., a mobile device of a passenger, a display device, a speaker within vehicle <NUM>), for example, using an infrared link, Bluetooth, etc. User interface system <NUM> may be part of peripheral devices implemented within vehicle <NUM> including, for example, a keyboard, a touch screen display device, a microphone, and a speaker, etc..

Some or all of the functions of autonomous vehicle <NUM> may be controlled or managed by perception and planning system <NUM>, especially when operating in an autonomous driving mode. Perception and planning system <NUM> includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs) to receive information from sensor system <NUM>, control system <NUM>, wireless communication system <NUM>, and/or user interface system <NUM>, process the received information, plan a route or path from a starting point to a destination point, and then drive vehicle <NUM> based on the planning and control information. Alternatively, perception and planning system <NUM> may be integrated with vehicle control system <NUM>.

For example, a user as a passenger may specify a starting location and a destination of a trip, for example, via a user interface. Perception and planning system <NUM> obtains the trip related data. For example, perception and planning system <NUM> may obtain location and route information from an MPOI server, which may be a part of servers <NUM>-<NUM>. The location server provides location services and the MPOI server provides map services and the POIs of certain locations. Alternatively, such location and MPOI information may be cached locally in a persistent storage device of perception and planning system <NUM>.

While autonomous vehicle <NUM> is moving along the route, perception and planning system <NUM> may also obtain real-time traffic information from a traffic information system or server (TIS). Note that servers <NUM>-<NUM> may be operated by a third party entity. Alternatively, the functionalities of servers <NUM>-<NUM> may be integrated with perception and planning system <NUM>. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system <NUM> (e.g., obstacles, objects, nearby vehicles), perception and planning system <NUM> can plan an optimal route and drive vehicle <NUM>, for example, via control system <NUM>, according to the planned route to reach the specified destination safely and efficiently.

Server <NUM> may be a data analytics system to perform data analytics services for a variety of clients. In one embodiment, data analytics system <NUM> includes data collector <NUM> and machine learning engine <NUM>. Data collector <NUM> collects driving statistics <NUM> from a variety of vehicles, either autonomous vehicles or regular vehicles driven by human drivers. Driving statistics <NUM> include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics <NUM> may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc..

Based on driving statistics <NUM>, machine learning engine <NUM> generates or trains a set of rules, algorithms, and/or predictive models <NUM> for a variety of purposes. In one embodiment, algorithms <NUM> may include an algorithm or model to perceive a driving environment, including detecting a first obstacle colliding with the ADV, an algorithm or model to determine a moving direction, an algorithm or model to determine a minimum deceleration, an algorithm or model to generate a brake command, and/or an algorithm or model to applying the brake command to the ADV, which will be described in details further below. Algorithms <NUM> can then be uploaded on ADVs to be utilized during autonomous driving in real-time.

<FIG> and <FIG> are block diagrams illustrating an example of a perception and planning system used with an autonomous vehicle according to one embodiment. System <NUM> may be implemented as a part of autonomous vehicle <NUM> of <FIG> including, but is not limited to, perception and planning system <NUM>, control system <NUM>, and sensor system <NUM>. Referring to <FIG>, perception and planning system <NUM> includes, but is not limited to, localization module <NUM>, perception module <NUM>, prediction module <NUM>, decision module <NUM>, planning module <NUM>, control module <NUM>, routing module <NUM>, collision module I <NUM>, collision module II <NUM>, and Brake module <NUM>.

Some or all of modules <NUM>-<NUM> may be implemented in software, hardware, or a combination thereof. For example, these modules may be installed in persistent storage device <NUM>, loaded into memory <NUM>, and executed by one or more processors (not shown). Note that some or all of these modules may be communicatively coupled to or integrated with some or all modules of vehicle control system <NUM> of <FIG>. Some of modules <NUM>-<NUM> may be integrated together as an integrated module.

Localization module <NUM> determines a current location of autonomous vehicle <NUM> (e.g., leveraging GPS unit <NUM>) and manages any data related to a trip or route of a user. Localization module <NUM> (also referred to as a map and route module) manages any data related to a trip or route of a user. A user may log in and specify a starting location and a destination of a trip, for example, via a user interface. Localization module <NUM> communicates with other components of autonomous vehicle <NUM>, such as map and route information <NUM>, to obtain the trip related data. For example, localization module <NUM> may obtain location and route information from a location server and a map and POI (MPOI) server. A location server provides location services and an MPOI server provides map services and the POIs of certain locations, which may be cached as part of map and route information <NUM>. While autonomous vehicle <NUM> is moving along the route, localization module <NUM> may also obtain real-time traffic information from a traffic information system or server.

Based on the sensor data provided by sensor system <NUM> and localization information obtained by localization module <NUM>, a perception of the surrounding environment is determined by perception module <NUM>. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc..

Perception module <NUM> may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of autonomous vehicle. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module <NUM> can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module <NUM> predicts what the object will behave under the circumstances. The prediction is performed based on the perception data perceiving the driving environment at the point in time in view of a set of map/rout information <NUM> and traffic rules <NUM>. For example, if the object is a vehicle at an opposing direction and the current driving environment includes an intersection, prediction module <NUM> will predict whether the vehicle will likely move straight forward or make a turn. If the perception data indicates that the intersection has no traffic light, prediction module <NUM> may predict that the vehicle may have to fully stop prior to enter the intersection. If the perception data indicates that the vehicle is currently at a left-turn only lane or a right-turn only lane, prediction module <NUM> may predict that the vehicle will more likely make a left turn or right turn respectively.

For each of the objects, decision module <NUM> makes a decision regarding how to handle the object. For example, for a particular object (e.g., another vehicle in a crossing route) as well as its metadata describing the object (e.g., a speed, direction, turning angle), decision module <NUM> decides how to encounter the object (e.g., overtake, yield, stop, pass). Decision module <NUM> may make such decisions according to a set of rules such as traffic rules or driving rules <NUM>, which may be stored in persistent storage device <NUM>.

Routing module <NUM> is configured to provide one or more routes or paths from a starting point to a destination point. For a given trip from a start location to a destination location, for example, received from a user, routing module <NUM> obtains route and map information <NUM> and determines all possible routes or paths from the starting location to reach the destination location. Routing module <NUM> may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line. The topographic maps are then provided to decision module <NUM> and/or planning module <NUM>. Decision module <NUM> and/or planning module <NUM> examine all of the possible routes to select and modify one of the most optimal routes in view of other data provided by other modules such as traffic conditions from localization module <NUM>, driving environment perceived by perception module <NUM>, and traffic condition predicted by prediction module <NUM>. The actual path or route for controlling the ADV may be close to or different from the reference line provided by routing module <NUM> dependent upon the specific driving environment at the point in time.

Based on a decision for each of the objects perceived, planning module <NUM> plans a path or route for the autonomous vehicle, as well as driving parameters (e.g., distance, speed, and/or turning angle), using a reference line provided by routing module <NUM> as a basis. That is, for a given object, decision module <NUM> decides what to do with the object, while planning module <NUM> determines how to do it. For example, for a given object, decision module <NUM> may decide to pass the object, while planning module <NUM> may determine whether to pass on the left side or right side of the object. Planning and control data is generated by planning module <NUM> including information describing how vehicle <NUM> would move in a next moving cycle (e.g., next route/path segment). For example, the planning and control data may instruct vehicle <NUM> to move <NUM> meters at a speed of <NUM> miles per hour (mph), then change to a right lane at the speed of <NUM> mph.

Based on the planning and control data, control module <NUM> controls and drives the autonomous vehicle, by sending proper commands or signals to vehicle control system <NUM>, according to a route or path defined by the planning and control data. The planning and control data include sufficient information to drive the vehicle from a first point to a second point of a route or path using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands) at different points in time along the path or route.

In one embodiment, the planning phase is performed in a number of planning cycles, also referred to as driving cycles, such as, for example, in every time interval of <NUM> milliseconds (ms). For each of the planning cycles or driving cycles, one or more control commands will be issued based on the planning and control data. That is, for every <NUM>, planning module <NUM> plans a next route segment or path segment, for example, including a target position and the time required for the ADV to reach the target position. Alternatively, planning module <NUM> may further specify the specific speed, direction, and/or steering angle, etc. In one embodiment, planning module <NUM> plans a route segment or path segment for the next predetermined period of time such as <NUM> seconds. For each planning cycle, planning module <NUM> plans a target position for the current cycle (e.g., next <NUM> seconds) based on a target position planned in a previous cycle. Control module <NUM> then generates one or more control commands (e.g., throttle, brake, steering control commands) based on the planning and control data of the current cycle.

Note that decision module <NUM> and planning module <NUM> may be integrated as an integrated module. Decision module <NUM>/planning module <NUM> may include a navigation system or functionalities of a navigation system to determine a driving path for the autonomous vehicle. For example, the navigation system may determine a series of speeds and directional headings to affect movement of the autonomous vehicle along a path that substantially avoids perceived obstacles while generally advancing the autonomous vehicle along a roadway-based path leading to an ultimate destination. The destination may be set according to user inputs via user interface system <NUM>. The navigation system may update the driving path dynamically while the autonomous vehicle is in operation. The navigation system can incorporate data from a GPS system and one or more maps so as to determine the driving path for the autonomous vehicle.

Although not shown, perception and planning system <NUM> further includes a data logger or data collector configured to collect data processed by the components <NUM>-<NUM> and sensors over a period of time, which may be stored as a part of data log <NUM>. For example, data log <NUM> includes any prediction, decision, and paths planned or made in each of the driving/planning cycle. Data log <NUM> may further include control commands issued and the responses or states (e.g., speed, acceleration, heading, locations, etc.) of the vehicle captured at different points in time during each driving cycle. Data log <NUM> may further include the obstacles or moving objects perceived over a period of time and their behaviors (e.g., prior locations, speed, headings captured during past cycles, etc.). Data log <NUM> may be utilized for planning and controlling the vehicle subsequently or alternatively, data log <NUM> may be analyzed offline for improving the driving algorithms or predictive models. The perception and planning system <NUM> may further include a brake rules/models for a set of brake rules/models.

<FIG> is a block diagram <NUM> illustrating an example of a perception module, a localization module and a control module of an ADV according to one embodiment. Referring to <FIG>, collision module I <NUM> in perception module <NUM> includes, but is not limited to, obstacle <NUM> and direction module <NUM>. Modules <NUM>-<NUM> may be integrated into a single module. Collision module II <NUM> in localization module <NUM> includes, but is not limited to, detection module <NUM> and direction module <NUM>. Modules <NUM>-<NUM> may be integrated into a single module. Brake module includes, but is not limited to, time module <NUM>, deceleration module <NUM>, gear module <NUM>, brake command generator <NUM> and brake command applicator <NUM>, which work together using brake rules or models <NUM> to control the ADV to avoid collision and softens an impact of the collision with an obstacle. Note that modules <NUM>-<NUM> may be integrated into fewer number of modules or a single module.

According to one embodiment, a driving environment is identified. The driving environment may be identified as a part of perception process performed by perception module <NUM> based on sensor data obtained from various sensors mounted on an ADV such as the sensors as shown in <FIG>. For example, obstacle module may be configured to detect a first obstacle in contact with the ADV. Detection module <NUM> in localization module <NUM> may be configured to detect the first obstacle colliding with the ADV. For example, Detection module <NUM> may use IMU to monitor sudden changes in acceleration (> <NUM> m/s<NUM>). Because the ADV cannot generate a larger than <NUM> m/s<NUM> acceleration/deceleration without being in a collision condition. Perception module <NUM> and localization module <NUM> may work together to detect a first obstacle colliding with the ADV. Based on a collision angle from the first obstacle, direction module <NUM> or <NUM> determine a current moving direction of the ADV. Direction module <NUM> or <NUM> may be included in perception module <NUM> or localization module <NUM>. Obstacle module <NUM> may be further configured to determine a second obstacle, which is the closest obstacle within a predetermined proximity of the moving direction.

In one embodiment, time module <NUM> is configured to determine whether a time duration when the ADV is to be in collision with the second obstacle is less than or equal to a predetermined time threshold. Deceleration module is configured to determining a minimum deceleration that is required for the ADV to avoid colliding with the second obstacle within the predetermined proximity of the moving direction. If the time duration when the ADV is to be in collision with the second obstacle is more than the predetermined time threshold, the minimum deceleration is zero. Otherwise, the minimum deceleration is calculated based on a difference between a speed of the ADV and a speed of the second obstacle, and a distance between the ADV and the second obstacle. Gear module <NUM> is configured to set a gear position of the ADV to a "Neutral" position. Brake command module <NUM> is configured to generate a brake command based on the minimum deceleration and apply the brake command to the ADV.

<FIG> illustrates a situation <NUM> of a collision of an ADV <NUM> with a first obstacle <NUM>. During the collision, it is important to reduce damage to both the ADV <NUM> and the first obstacle <NUM>, and avoid to cost damage to other obstacles (e.g., <NUM>). An obstacle may be a vehicle, motorcycle, bicycle, or pedestrian, etc. Currently, an ADV is configured to apply a brake during a collision, which may not always be a good solution. As illustrated in <FIG>, when the ADV <NUM> is being hit from behind by the first obstacle <NUM>, the ADV <NUM> may not be able to absorb an impact or energy from the collision by applying the brake. There may be a need to develop a post collision damage reduction method.

Disclosed herein is a post collision damage reduction method by manipulating a brake system of the ADV. By this method, the ADV may absorb energy from the collision to soften the impact of the collision with the first obstacle and avoid collisions with other obstacles (e.g., <NUM>). For example, when the ADV <NUM> is being hit from behind by the first obstacle <NUM>, it may be advantageous to release the brake, such that the ADV <NUM> may slide accordingly to absorb the energy from the collision.

At first, a collision condition may be determined by the ADV <NUM>. For example, obstacle module <NUM> may detect that the first obstacle <NUM> is in contact with the ADV <NUM>. Detection module <NUM> in localization module <NUM> may be configured to detect the first obstacle colliding with the ADV. For example, an IMU in localization module <NUM> of the ADV <NUM> may monitor sudden changes in an acceleration or deceleration of the ADV <NUM>. Because the ADV <NUM> is not able to generate an acceleration or deceleration larger than <NUM> m/s<NUM> without being in a collision condition. When the IMU detects an acceleration or deceleration larger than <NUM> m/s<NUM> and/or the first obstacle <NUM> is in contact with the ADV <NUM>, it may be determined that the first obstacle <NUM> is colliding with the ADV <NUM>.

A current moving direction <NUM> of the ADV may be determined. For example, direction module <NUM> in perception module <NUM> or direction module <NUM> in localization module <NUM> may calculate the current moving direction <NUM> of the ADV <NUM> based on a collision angle from the first obstacle <NUM>. The collision angle may refer to an angle between a moving direction of the ADV <NUM> and a moving direction of the first obstacle <NUM> at a time of the collision. The current moving direction may be calculated according to the momentum conservation law. A closest obstacle <NUM> within a predetermined proximity of the moving direction <NUM> may also be determined. For example, the closest obstacle <NUM> within the predetermined proximity of the moving direction <NUM> may be determined by the obstacle module <NUM>. For example, the predetermined proximity may be <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> degrees or any values therebetween. The distance <NUM> between the ADV <NUM> and the second obstacle <NUM> may also be determined by the obstacle module <NUM>. A gear position of the ADV may be set to a "Neutral" position, for example, by the gear module <NUM>.

A time duration to a second collision to the closest obstacle <NUM> may be determined. A minimum deceleration to avoid a second collision with the closest obstacle <NUM> may be determined. If the time duration to the second collision is larger than a predetermined time threshold, for example, <NUM> seconds, the closest obstacle <NUM> is not with a proximity of the ADV. Thus, the minimum deceleration may be determined to be zero. A deceleration may not be needed or a minimum deceleration of "zero" is determined. Accordingly, a zero brake command may be generated. The ADV may not need to have a brake. In this situation, it is advantageous to generate a "zero" brake command, such that the ADV <NUM> may slide accordingly to absorb the energy from the collision and softens the impact of the collision with the first obstacle <NUM>.

If the time duration to the second collision is less than or equal to the predetermined time threshold, for example, <NUM> seconds, a minimal deceleration required to avoid the second collision may be calculated. For example, the minimum deceleration may be calculated based on a difference between a speed of the ADV <NUM> and a speed of the second obstacle <NUM>, and the distance <NUM> between the ADV <NUM> and the second obstacle <NUM>. The minimum deceleration may be calculated by the following equation: <MAT>
where "a" is a deceleration of the ADV <NUM>, "v_difference" is a difference between a speed of the ADV <NUM> and a speed of the second obstacle <NUM>, and "distance"is the distance <NUM> between the ADV <NUM> and the second obstacle <NUM>.

A deceleration command required to provide the minimum deceleration may be generated. Then, the deceleration command may be applied to the ADV <NUM> such that the ADV <NUM> may decelerate to avoid the second collision to the closest obstacle <NUM>.

<FIG> is a processing flow diagram <NUM> illustrating an example of reducing post collision damage according to one embodiment.

At operation <NUM>, a collision condition may be determined by an ADV (e.g., <NUM>). For example, obstacle module <NUM> may detect that the first obstacle (e.g., <NUM>) is in contact with the ADV. Detection module <NUM> in localization module <NUM> may be configured to detect the first obstacle colliding with the ADV. For example, an IMU in localization module <NUM> of the ADV (e.g., <NUM>) may monitor sudden changes in an acceleration or deceleration of the ADV. When the IMU detects an acceleration or deceleration larger than <NUM> m/s<NUM> and/or the first obstacle <NUM> is in contact with the ADV (e.g., <NUM>), it may be determined that the first obstacle (e.g., <NUM>) is colliding with the ADV (e.g., <NUM>).

At operation <NUM>, a current moving direction (e.g., <NUM>) of the ADV (e.g., <NUM>) may be determined. For example, direction module <NUM> in perception module <NUM> or direction module <NUM> in localization module <NUM> may calculate the current moving direction (e.g., <NUM>) of the ADV based on a collision angle from the first obstacle. The current moving direction may be calculated according to the momentum conservation law.

At operation <NUM>, a closest obstacle (e.g., <NUM>) within a predetermined proximity of the moving direction (e.g., <NUM>) may also be determined. The distance (e.g., <NUM>) between the ADV (e.g., <NUM>) and the second obstacle (e.g., <NUM>) may also be determined by the obstacle module <NUM>. At operation <NUM>, a gear position of the ADV may be set to a "Neutral" position, for example, by the gear module <NUM>. At operation <NUM>, it is determined whether a time duration to a second collision to the closest obstacle (e.g., <NUM>) is more than a predetermined time threshold, for example, <NUM> seconds.

At operation <NUM>, the minimum deceleration may be determined to be zero in response to determining that the time duration to the second collision is more than the predetermined time threshold. At operation <NUM>, a brake command of "zero" may be generated. At operation <NUM>, the brake command of "zero" may be applied to allow the ADV (e.g., <NUM>) to slide accordingly to absorb the energy from the collision and softens the impact of the collision with the first obstacle.

At operation <NUM>, in response to determining that the time duration to the second collision is less than or equal to the predetermined time threshold, for example, <NUM> seconds, a minimum deceleration required to avoid the second collision may be calculated. For example, the minimum deceleration may be calculated based on a difference between a speed of the ADV (e.g., <NUM>) and a speed of the second obstacle (e.g., <NUM>), and the distance (e.g., <NUM>) between the ADV and the second obstacle.

At operation <NUM>, a deceleration command required to provide the minimum deceleration may be generated. At operation <NUM>, the deceleration command may be applied to the ADV (e.g., <NUM>) such that the ADV may decelerate to avoid the second collision to the closest obstacle (e.g., <NUM>).

<FIG> is a flow diagram <NUM> illustrating an example of a process for reducing post collision according to one embodiment. Process <NUM> may be performed by processing logic which may include software, hardware, or a combination thereof. For example, process <NUM> may be performed by localization module <NUM>, perception module <NUM> and/or control module <NUM>. Referring to <FIG>, in operation <NUM>, processing logic detects a first obstacle (e.g., vehicles, motorcycles, bicycles) colliding with the ADV. In operation <NUM>, processing logic determines a minimum deceleration that is required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of a moving direction. In operation <NUM>, processing logic generates a brake command based on the minimum deceleration. In operation <NUM>, processing logic applies the brake command to the ADV, such that the ADV avoids collision with the second obstacle and softens an impact of the collision with the first obstacle.

Note that some or all of the components as shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components can be implemented as software installed and stored in a persistent storage device, which can be loaded and executed in a memory by a processor (not shown) to carry out the processes or operations described throughout this application. Alternatively, such components can be implemented as executable code programmed or embedded into dedicated hardware such as an integrated circuit (e.g., an application specific IC or ASIC), a digital signal processor (DSP), or a field programmable gate array (FPGA), which can be accessed via a corresponding driver and/or operating system from an application. Furthermore, such components can be implemented as specific hardware logic in a processor or processor core as part of an instruction set accessible by a software component via one or more specific instructions.

Embodiments of the invention also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

Embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the invention as described herein.

Claim 1:
A computer-implemented method for operating an autonomous driving vehicle (ADV), the method comprising:
detecting (<NUM>, <NUM>) a first obstacle colliding with the ADV;
determining whether a time duration when the ADV is to be in collision with a second obstacle is more than a predetermined time threshold;
determining (<NUM>, <NUM>) a minimum deceleration to be zero in response to determining that the time duration when the ADV is to be in collision with a second obstacle is more than a predetermined time threshold, wherein the minimum deceleration is required for the ADV to avoid colliding with a second obstacle within a predetermined proximity of a moving direction;
generating (<NUM>, <NUM>) a brake command of "zero" based on the minimum deceleration; and
applying (<NUM>, <NUM>) the brake command of "zero" to the ADV, such that the ADV slides accordingly to absorb the energy from a collision with the first obstacle and softens an impact of the collision with the first obstacle.