Method and system for detecting operator braking intention

Abstract

Methods and systems for detecting operator braking intent are disclosed. A brake control system determines a brake pedal travel value and a brake actuation position based on a brake pedal travel sensor and a motor actuation sensor of an autonomous driving vehicle (ADV). The brake control system determines a first threshold value based on the brake actuation position. The brake control system determines that a deviation of the observed brake pedal travel value from the brake actuation position or an original brake pedal sensor value is above the first threshold value, and in response to determining that the deviation is above the first threshold value, detects an intent of an operator to apply a brake control. In this way, a brake intervention can be detected before a steady state, and the detection of the operator intervention is robust and reliable.

Description

Technical Field

[0001] The embodiments of this disclosure generally relate to operating autonomous vehicles. More specifically, embodiments of the invention relate to operator braking detection in autonomous vehicles. Background Technology

[0002] Vehicles operating in autonomous mode (e.g., driverless) can reduce some of the driving-related responsibilities for passengers, especially the driver. When operating in autonomous mode, the vehicle can use onboard sensors to navigate to various locations, allowing the vehicle to operate with minimal human-machine interaction or even without any passengers.

[0003] Braking control is a critical operation in autonomous driving. During an autonomous driving (AD) event, if the operator applies the brakes, the pressure request from the Autonomous Driving System (ADS) should be immediately cancelled. However, since deceleration requests from AD and driver requests typically use the same sensor inputs, a highly robust and sensitive driver braking intervention detection mechanism is essential for AD. Summary of the Invention

[0004] On the one hand, a method for detecting an operator's braking intention is provided, comprising:

[0005] Brake pedal travel value is determined based on brake pedal travel sensor of autonomous vehicle (ADV);

[0006] The braking actuation position is determined based on the actuation sensor of the ADV;

[0007] A first threshold is determined based on the braking actuation position;

[0008] Determining that the deviation between the brake pedal travel value and the brake actuation position is higher than the first threshold; and

[0009] In response to determining that the deviation is higher than the first threshold, the operator's intention to apply braking control is detected.

[0010] On the other hand, another method for detecting operator braking intention is provided, including:

[0011] Determine the first and second brake pedal travel values ​​corresponding to the first and second planning periods, respectively;

[0012] Determine the difference between the first and second brake pedal travel values; and

[0013] In response to determining that the difference between the first and second brake pedal travel values ​​is higher than a predetermined threshold, an operator's intention to apply brake control is detected.

[0014] On the other hand, a non-transitory machine-readable medium is provided, wherein instructions are stored that, when executed by a processor, cause the processor to perform the operation of the method for detecting an operator's braking intention as described above.

[0015] On the other hand, a data processing system is provided, including:

[0016] Processor; and

[0017] The memory is coupled to the processor and stores instructions that, when executed by the processor, cause the UC nursing period to perform the operation of the method described above for detecting the operator's braking intention.

[0018] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, causes the processor to perform the operation of the method for detecting operator braking intent as described above.

[0019] The present invention enables the detection of braking intervention before a steady state, and the detection of operator intervention is robust and reliable. Attached Figure Description

[0020] Embodiments of this disclosure are shown by way of example and are not limited to the figures in the accompanying drawings, in which the same reference numerals denote similar elements.

[0021] Figure 1 This is a block diagram illustrating a networking system according to one embodiment.

[0022] Figure 2 This is a block diagram illustrating an example of an autonomous driving vehicle according to one embodiment.

[0023] Figures 3A-3B This is a block diagram illustrating an example of an autonomous driving system used with an autonomous vehicle according to one embodiment.

[0024] Figure 4 This is a block diagram illustrating an example of a braking intent module according to one embodiment.

[0025] Figure 5 This is a block diagram illustrating an example of a hydraulic braking system according to one embodiment.

[0026] Figure 6A This is a block diagram illustrating an example of a braking control system according to one embodiment.

[0027] Figure 6B This is a block diagram illustrating an example of a brake pedal observation module according to one embodiment.

[0028] Figure 7This is a block diagram illustrating an example of detecting braking intent in a steady state according to one embodiment.

[0029] Figure 8 This is a block diagram illustrating an example of detecting braking intent during a deceleration request according to one embodiment.

[0030] Figure 9 This is a block diagram illustrating an example of detecting braking intent during brake holding according to one embodiment.

[0031] Figure 10 This is a block diagram illustrating an example of detecting braking intent during brake release according to one embodiment.

[0032] Figure 11 This is a block diagram illustrating an example of detecting braking intent during a deceleration request with two or more reliable braking thrusts according to one embodiment.

[0033] Figure 12 This is a block diagram illustrating an example of detecting braking intent during a ramp deceleration request according to one embodiment.

[0034] Figure 13A This is a block diagram illustrating a brake drive mapping table according to one embodiment. Figure 13B It is shown Figure 13A A diagram of the brake drive mapping table.

[0035] Figure 14 This is a flowchart illustrating a method for detecting an operator's braking intention according to one embodiment.

[0036] Figure 15 This is a flowchart illustrating a method for detecting an operator's braking intention according to another embodiment. Detailed Implementation

[0037] Various embodiments and aspects of this disclosure will be described with reference to the details of the following discussion, and the accompanying drawings will illustrate various embodiments. The following description and drawings are illustrative of this disclosure and should not be construed as limiting it. Numerous specific details are described to provide a full understanding of the various embodiments of this disclosure. However, in some cases, well-known or conventional details have not been described in order to provide a brief discussion of embodiments of this disclosure.

[0038] References to "an embodiment" or "embodiment" in the specification mean that a particular feature, structure, or characteristic described in connection with that embodiment may be included in at least one embodiment of this disclosure. The phrase "in an embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment.

[0039] According to some embodiments, the brake control system detects operator braking intervention by measuring the pedal travel value and the brake booster motor actuation position. If operator intervention / engagement of the brakes is determined, the brake control system signals the Autonomous Driving System (ADS) to cancel the Autonomous Driving (AD) event and returns the ADV operation to the operator so that the operator can manually control the ADV.

[0040] Braking control can be measured by the pedal travel distance sensor of the brake booster in the braking system. When the ADS requests different types of braking AD events (hard deceleration, brake holding, brake release, ramp deceleration, etc.), the measured pedal travel distance sensor value can reflect both the operator's manual intervention and the AD event, because the pedal travel sensor value reflects the braking control operated by both the ADS and the operator.

[0041] Currently, when operators intervene, the measurements are not sensitive to the degree of pedal travel (requiring operators to register large pedal travel distances), and the measurements are slow (exceeding one planning cycle). Figure 7 An example of such operator intent detection is shown. For example... Figure 7 As shown, signal 701 can be a pedal travel distance requested by one or more AD events of the ADS, signal 703 can be a measured pedal travel distance value from the pedal travel sensor, and signal 705 can be a measured actuation position of the electric motor that is boosting / actuating the brake. When the ADS requests deceleration (e.g., signal 701 is requested at a target value), observation signal 703 can be measured in each planning cycle to detect whether observation signal 703 is higher than the requested target value. If it is determined that signal 703 is higher than the target value, operator intervention in brake control is detected. Note that the brake booster improves braking performance. The brake booster makes braking easier for the driver by increasing the applied force without requiring additional force on the pedal.

[0042] As mentioned above, reliably detecting operator intervention using current detection methods can be slow because the braking control system must wait for the braking control to stabilize to a steady state, for example, between time t1 and t2, before the ADS braking control can reliably distinguish from operator intervention. Furthermore, within the time window between time t0 and t1, the detection of operator intent is insensitive because if the operator depresses the brake pedal, the intervention may or may not be recorded; for example, operator intervention can only be detected if the operator depresses the brake pedal sufficiently to record a large pedal travel distance (> the target value). Therefore, it is necessary to isolate the detection of operator braking control from the ADS braking control detection to achieve robust operator intervention detection.

[0043] According to the first aspect, the braking control system determines and observes the brake pedal travel value and brake actuation position based on the brake pedal travel sensor and motor actuation sensor of the autonomous vehicle (ADV). The braking control system determines a first threshold based on the brake actuation position. The braking control system determines that the deviation between the observed brake pedal travel value and the brake actuation position is higher than the first threshold, and in response to determining that the deviation is higher than the first threshold, detects the operator's intention to apply brake control. In one embodiment or alternative, the braking control system determines that the deviation between the observed brake pedal travel value and the original pedal travel sensor value is higher than a threshold, and in response to determining that the deviation is higher than this threshold, detects the operator's intention to apply brake control. This method also allows for robust and reliable detection of brake intervention.

[0044] According to the second aspect, the braking control system determines first and second brake pedal travel values ​​for the autonomous vehicle (ADV) corresponding to the first and second planning cycles, respectively. The braking control system determines the difference between the first and second brake pedal travel values. In response to determining that the difference between the first and second brake pedal travel values ​​is higher than a predetermined threshold, the braking control system detects the operator's intention to apply braking control. In this scenario, braking intervention can be robustly and reliably detected.

[0045] Figure 1 This is a block diagram illustrating an autonomous driving network configuration according to an embodiment of the present disclosure. (See reference...) Figure 1 Network configuration 100 includes an autonomous vehicle (ADV) 101, which can be communicatively coupled to one or more servers 103-104 via network 102. Although only one ADV is shown, multiple ADVs can be coupled to each other and / or to servers 103-104 via network 102. Network 102 can be any type of network, 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. Servers 103-104 can be any type of server or server cluster, such as a web or cloud server, an application server, a backend server, or a combination thereof. Servers 103-104 can be data analytics servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc.

[0046] ADV refers to a vehicle that can be configured to operate in an autonomous mode, in which the vehicle navigates its environment with little or no driver input. Such an ADV may include a sensor system with one or more sensors configured to detect information about the environment in which the vehicle operates. The vehicle and its associated controller use the detected information to navigate through the environment. ADV 101 can operate in manual mode, fully autonomous mode, or partially autonomous mode.

[0047] In one embodiment, ADV 101 includes, but is not limited to, an autonomous driving system (ADS) 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, and a sensor system 115. ADV 101 may also include certain common components found in ordinary vehicles, such as an engine, wheels, steering wheel, transmission, etc., which can be controlled by the vehicle control system 111 and / or ADS 110 using various communication signals and / or commands (e.g., acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc.).

[0048] Components 110-115 can be communicatively coupled to each other via interconnect, bus, network, or a combination thereof. For example, components 110-115 can be communicatively coupled to each other via a Controller Area Network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in masterless applications. It is a message-based protocol originally designed for multiplexing electrical wiring in automobiles, but is also used in many other environments.

[0049] Now for reference Figure 2In one embodiment, the sensor system 115 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU) 213, a radar unit 214, and a light detection and range (LIDAR) unit 215. The GPS system 212 may include a transceiver operable to provide information about the ADV's location. The IMU unit 213 may sense changes in the ADV's position and orientation based on inertial acceleration. The radar unit 214 may represent a system that uses radio signals to sense objects within the ADV's local environment. In some embodiments, in addition to sensing objects, the radar unit 214 may additionally sense the velocity and / or heading of objects. The LIDAR unit 215 may use lasers to sense objects in the ADV's environment. The LIDAR unit 215 may include one or more laser sources, a laser scanner, and one or more detectors, as well as other system components. The camera 211 may include one or more devices to capture images of the environment surrounding the ADV. The camera 211 may be a still camera and / or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating and / or tilting platform.

[0050] The sensor system 115 may also include other sensors, such as sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and audio sensors (e.g., microphones). The audio sensor can be configured to capture sound from the environment surrounding the ADV. The steering sensor can be configured to sense the steering angle of the steering wheel, the vehicle's wheels, or a combination thereof. The throttle and brake sensors sense the vehicle's throttle and brake positions, respectively. In some cases, the throttle and brake sensors can be integrated into an integrated throttle / brake sensor.

[0051] In one embodiment, the vehicle control system 111 includes, but is not limited to, a steering unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a braking unit 203. The steering unit 201 is used to adjust the direction or heading of the vehicle. The throttle unit 202 is used to control the speed of a motor or engine, which in turn controls the speed and acceleration of the vehicle. The braking unit 203 decelerates the vehicle by providing friction to slow down the wheels or tires. Note that... Figure 2 The components shown can be implemented in hardware, software, or a combination thereof.

[0052] Return to reference Figure 1The wireless communication system 112 allows communication between ADV 101 and external systems, such as devices, sensors, other vehicles, etc. For example, the wireless communication system 112 can communicate wirelessly with one or more devices directly or via a communication network, such as communicating with servers 103-104 via network 102. The wireless communication system 112 can use any cellular communication network or wireless local area network (WLAN), such as using WiFi, to communicate with another component or system. The wireless communication system 112 can communicate directly with devices (e.g., passenger mobile devices, display devices, speakers within vehicle 101), for example, using infrared links, Bluetooth, etc. The user interface system 113 can be part of peripheral devices implemented within vehicle 101, including, for example, a keyboard, touchscreen display, microphone, and speakers.

[0053] Some or all of the functions of ADV 101 can be controlled or managed by ADS 110, especially when operating in autonomous driving mode. ADS 110 includes the necessary hardware (e.g., processor, memory, storage devices) and software (e.g., operating system, planning and routing programs) to receive information from sensor system 115, control system 111, wireless communication system 112, and / or user interface system 113, process the received information, plan a route or path from the origin to the destination, and then drive vehicle 101 based on the planning and control information. Alternatively, ADS 110 can be integrated with vehicle control system 111.

[0054] For example, a passenger can specify the start and destination of their trip via a user interface. The ADS110 obtains trip-related data. For instance, the ADS110 can obtain location and route information from an MPOI server, which may be part of servers 103-104. The location server provides location services, and the MPOI server provides map services and points of interest (POIs) for certain locations. Alternatively, this location and MPOI information can be cached locally in the ADS110's persistent storage.

[0055] As ADV 101 moves along the route, ADS 110 can also obtain real-time traffic information from a traffic information system or server (TIS). Note that servers 103-104 can be operated by a third-party entity. Alternatively, the functionality of servers 103-104 can be integrated with ADS 110. Based on real-time traffic information, MPOI information, and location information, as well as real-time local environmental data (e.g., obstacles, objects, nearby vehicles) detected or sensed by sensor system 115, ADS 110 can plan an optimal route and, for example, drive vehicle 101 according to the planned route via control system 111 to safely and efficiently reach the designated destination.

[0056] Figure 3A and 3B This is a block diagram illustrating an example of an autonomous driving system used with an ADV according to one embodiment. System 300 can be implemented as follows: Figure 1 The ADV 101 includes, but is not limited to, ADS 110, control system 111, and sensor system 115. (See reference...) Figures 3A-3B The ADS 110 includes, but is not limited to, a positioning module 301, a perception module 302, a prediction module 303, a decision-making module 304, a planning module 305, a control module 306, a routing module 307, and a braking intention module.

[0057] Some or all of modules 301-308 may be implemented in software, hardware, or a combination thereof. For example, these modules may be installed in permanent storage device 352, loaded into memory 351, and executed by one or more processors (not shown). Note that some or all of these modules may be communicatively coupled to... Figure 2 Some or all of the modules of the vehicle control system 111, or integrated therewith. Some of the modules 301-308 can be integrated together as an integrated module.

[0058] The positioning module 301 determines the current location of the ADV 300 (e.g., using GPS unit 212) and manages any data related to the user's trip or route. The positioning module 301 (also called the map and route module) manages any data related to the user's trip or route. The user can log in, for example, via a user interface and specify the start and destination of the trip. The positioning module 301 communicates with other components of the ADV 300, such as map and route information 311, to obtain trip-related data. For example, the positioning module 301 can obtain location and route data from a location server and a map and POI (MPOI) server. The location server provides location services, and the MPOI server provides map services and POIs for certain locations, which can be cached as part of the map and route data 311. As the ADV 300 moves along the route, the positioning module 301 can also obtain real-time traffic information from a traffic information system or server.

[0059] Based on sensor data provided by sensor system 115 and positioning information obtained by positioning module 301, perception module 302 determines the perception of the surrounding environment. The perception information can represent the situation around the vehicle being driven by a typical driver. Perception may include lane configuration, traffic light signals, and the relative positions of objects such as other vehicles, pedestrians, buildings, crosswalks, or other traffic-related signs (e.g., stop signs, yield signs). Lane configuration includes information describing one or more lanes, such as, for example, the shape of the lane (e.g., straight or curved), the width of the lane, the number of lanes in the road, one-way or two-way lanes, merging or separating lanes, lane exits, etc.

[0060] The perception module 302 may include a computer vision system or the functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and / or features in the ADV's environment. Objects may include traffic signals, lane boundaries, other vehicles, pedestrians and / or obstacles, etc. The computer vision system may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track objects, and estimate the velocity of objects, etc. The perception module 302 may also detect objects based on additional sensor data provided by other sensors such as radar and / or LIDAR.

[0061] For each object, prediction module 303 predicts how the object will behave in the environment. Given a set of map / route information 311 and traffic rules 312, predictions are performed based on perceived data of the driving environment at a given point in time. For example, if the object is a vehicle traveling in the opposite direction and the current driving environment includes an intersection, prediction module 303 will predict whether the vehicle is likely to move straight ahead or turn. If the perceived data indicates that there are no traffic lights at the intersection, prediction module 303 can predict that the vehicle may have to come to a complete stop before entering the intersection. If the perceived data indicates that the vehicle is currently in a left-turn-only lane or a right-turn-only lane, prediction module 303 can predict that the vehicle is more likely to make a left turn or a right turn, respectively.

[0062] For each object, decision module 304 makes a decision about how to handle that object. For example, given a specific object (e.g., another vehicle at an intersection) and metadata describing that object (e.g., speed, direction, steering angle), decision module 304 decides how to encounter the object (e.g., overtake, yield, stop, pass). Decision module 304 may make these decisions based on a set of rules, such as traffic rules or driving rules 312, which may be stored in permanent storage device 352.

[0063] The routing module 307 is configured to provide one or more routes or paths from the origin to the destination. For a given trip from the origin to the destination received from the user, for example, the routing module 307 obtains route and map information 311 and determines all possible routes or paths from the origin to the destination. The routing module 307 can generate reference lines in the form of topographic maps for each route it determines from the origin to the destination. The reference lines refer to ideal routes or paths free from any interference from other vehicles, obstacles, or traffic conditions. That is, if there are no other vehicles, pedestrians, or obstacles on the road, the ADV should follow the reference lines precisely or closely. The topographic map is then provided to the decision module 304 and / or the planning module 305. The decision module 304 and / or the planning module 305 examine all possible routes to select and modify one of the optimal routes based on other data provided by other modules (such as traffic conditions from the positioning module 301, the driving environment perceived by the perception module 302, and traffic conditions predicted by the prediction module 303). Depending on the specific driving conditions at a given point in time, the actual path or route used to control the ADV may be close to or different from the reference line provided by the routing module 307.

[0064] Based on the decision for each perceived object, the planning module 305 uses reference lines provided by the routing module 307 as a basis to plan the path or route for ADV, along with driving parameters (e.g., distance, speed, and / or steering angle). That is, for a given object, the decision module 304 decides what to do with that object, while the planning module 305 determines how to do it. For example, for a given object, the decision module 304 might decide to pass the object, while the planning module 305 might determine whether to pass to the left or right of the object. Planning and control data is generated by the planning module 305 and includes information describing how vehicle 101 will move in the next movement cycle (e.g., the next route / path segment). For example, the planning and control data might instruct vehicle 101 to move 10 meters at 30 miles per hour (mph) and then change lanes to the right at 25 mph.

[0065] Based on planning and control data, control module 306 controls and drives the ADV by sending appropriate commands or signals to vehicle control system 111 according to the route or path defined by the planning and control data. The planning and control data includes sufficient information to drive the vehicle from one point to another along the route or path at different times using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands).

[0066] In one embodiment, the planning phase is performed within multiple planning cycles (also known as driving cycles, such as in each time interval of 100 milliseconds (ms)). For each planning cycle or driving cycle, one or more control commands are issued based on planning and control data. That is, for every 100 ms, the planning module 305 plans the next route segment or path segment, including, for example, the target location and the time required for the ADV to reach the target location. Alternatively, the planning module 305 may also specify specific speeds, directions, and / or steering angles, etc. In one embodiment, the planning module 305 plans a route segment or path segment for the next predetermined time period, such as 5 seconds. For each planning cycle, the planning module 305 plans a target location for the current cycle (e.g., the next 5 seconds) based on the target location planned in the previous cycle. The control module 306 then generates one or more control commands (e.g., throttle, braking, steering control commands) based on the planning and control data of the current cycle.

[0067] Note that the decision module 304 and the planning module 305 can be integrated into an integrated module. The decision module 304 / planning module 305 may include a navigation system or the functionality of a navigation system to determine a driving path for the ADV. For example, the navigation system may determine a series of speed and heading parameters to influence the movement of the ADV along a path that substantially avoids perceived obstacles, while generally guiding the ADV along a road-based path leading to the final destination. The destination can be set based on user input via the user interface system 113. The navigation system can dynamically update the driving path while the ADV is in operation. The navigation system may incorporate data from a GPS system and one or more maps to determine the driving path for the ADV.

[0068] Figure 4 This is a block diagram illustrating an example of a brake intention module 308 according to one embodiment. The brake intention module 308 can detect an operator's intention to apply brake control. In one embodiment, the brake intention module 308 may include a pedal travel determiner module 401, a motor actuation determiner module 402, a threshold determiner module 403, a deviation module 404, an intention detection module 405, an AD cancellation event module 406, and a brake pedal observation module 407. The pedal travel determiner module 401 can determine and observe pedal travel distance sensor values ​​by obtaining measurement readings from a brake pedal travel sensor in the brake booster. The motor actuation determiner module 402 can determine the motor actuation position of the motor in the brake booster, which is electronically controlled by a motor actuation sensor to measure the brake pedal travel distance from the motor actuation sensor. The threshold determiner module 403 can obtain mapping values ​​from a mapping table, such as..., using the measured motor actuation position. Figure 3AThe mapping table 313 is used to calculate a threshold. The threshold may correspond to a threshold where the pedal travel distance sensor value needs to exceed to record operator intervention, or a threshold where the difference between the pedal travel distance sensor value and the actuation position needs to exceed to record operator intervention. The deviation module 404 can determine the deviation value of any one of the brake pedal travel sensor, actuation position, and threshold. The intent detection module 405 can use the deviation value to detect operator intervention. The AD event cancellation module 406 can cancel AD events that have been issued or are awaiting issuance to the AD. Here, AD events may include acceleration requests, driving steering requests, signal requests, deceleration requests, etc. The brake pedal observation module 407 can determine the observed brake pedal travel value. (See reference...) Figure 5-14 Further describe the operation of modules 401-407.

[0069] Figure 5 A representation of a vehicle's hydraulic braking system 500 is shown. The vehicle braking system 500 may include a front axle braking circuit 2 and a rear axle braking circuit 3 for actuating wheel brakes (not shown) of the wheels for an ADV using hydraulically controlled brake fluid. Brake circuits 2 and 3 may be connected to a master brake cylinder 4, which is supplied with brake fluid by a brake fluid reservoir 5. The master brake cylinder piston within the master brake cylinder 4 can be operated via a brake pedal 6.

[0070] In one embodiment, the braking system 500 includes a brake booster 10 coupled between the brake pedal 6 and the master brake cylinder 4. The booster 10 may include an electric motor 11, a mechanical gearbox 12, and an electronic control unit (ECU) 14. The ECU 14 may represent a microcontroller that controls the actuation of the booster 10. The booster 10 can enhance brake control applied by the operator. For example, the brake pedal travel distance applied by the operator can be measured by a pedal travel sensor 7. The signal from the pedal travel sensor 7 can be transmitted to the ECU 14 of the booster 10, causing the gears of the mechanical gearbox 12 to rotate, thereby pushing the applied brake pedal and increasing the hydraulic braking pressure at the master brake cylinder 4. In one embodiment, the actuation position of the electric motor 11 can be measured by an actuation sensor 13.

[0071] Brake fluid may be carried in each brake circuit 2, 3 and supplied to the braking devices (not shown) of the vehicle wheels. The brake hydraulic system may further include a hydraulic pump (not shown) to control the hydraulic braking pressure of the brake hydraulic system.

[0072] In autonomous driving mode, ADS can request brake control (e.g., pedal travel distance) by sending a signal to the ECU 14 of booster 10 to cause the gears of the mechanical transmission 12 to rotate and actuate the piston of the master brake cylinder 4. Additionally, the brake control system can obtain sensor values ​​from the ECU 14 of booster 10 to obtain measurements from the travel sensor 7 and / or actuation sensor 13.

[0073] Although the vehicle braking hydraulic system is described using the term "brake fluid hydraulic," the embodiments are not limited to fluid hydraulic brakes. For example, an electronic braking system can be used instead of a fluid hydraulic braking system.

[0074] Figure 6A This is a block diagram illustrating an example of a brake control system 600 according to one embodiment. The brake control system 600 may be a modified braking system (e.g., signal P1 is routed to brake intention module 308) that actuates the brakes based on an AD event or a brake pedal sensor signal from P1. For example, when operator intervention is detected, the brake control system 600 may cancel the AD event, clear the event queue buffer of the ADDS, and actuate the brakes based on a manual braking operation by the operator detected at P1. When no operator intervention is detected, the brake control system 600 may actuate the brakes based on an AD event.

[0075] In one embodiment, the braking control system 600 may include a braking intent module 308, a braking system 605, and a brake actuator 606. The braking intent module 308 may receive a brake pedal sensor value P1, a brake actuator position sensor value A1, and an ADS signal AD1, and determine a signal M1 to be sent to the braking system 605. M1 may represent P1 when the braking intent module 308 detects operator intervention, or M1 may represent AD1 when the braking intent module 308 detects non-interventional autonomous driving operation. An actuation sensor measuring the actuation position value A1 may be associated with the brake actuator 606; for example, the actuation position sensor may be in a feedback loop with the ADV's autonomous driving system (ADS).

[0076] Figure 6B This is a block diagram illustrating an example of a brake pedal observation module 407 according to one embodiment. As shown, the brake pedal observation module 407 can receive inputs from a pedal travel sensor P1, a pressure request AD1, actuator information A1 (e.g., actuator position, power applied to the actuator, actuator state (on, off, etc.), vehicle state 651 (e.g., acceleration, deceleration, etc.), and AD kit 653. Using the combination of inputs, the brake pedal observation module 407 can determine an observed brake pedal distance value 655. The observed brake pedal distance value 655 can correspond to... Figure 8 803 in Figure 9 903 in Figure 10 1003 in Figure 11 1103 in Figure 12 1203 in the middle.

[0077] In some embodiments, the braking control system 600 may be implemented in software, hardware, or a combination thereof. For example, the software portion of the braking control system 600 may be installed in permanent storage device 352, loaded into memory 351, and executed by one or more processors (not shown). Note that the braking control system 600 may be communicatively coupled to... Figure 2 Part or all modules of the vehicle control system 111 and / or Figure 3A Modules 301-308 or integrated with them.

[0078] Figure 8 This is a block diagram illustrating example 800 of detecting braking intervention during a deceleration request according to one embodiment. The detection of braking intent / intervention can be achieved through... Figure 4 Operator intent module 308 or Figure 6A The braking control system is activated. For example... Figure 8 As shown, when the ADS requests braking operation, it requests an AD event for deceleration, and this AD event can be received by module 308. The requested pedal travel control 801 of the AD event can be represented by an upward ramp curve, where the requested target control value Target is requested before time t3. As shown, when the requested pedal travel distance value 801 equals the observed pedal travel distance value 803, the braking system reaches steady state at time = t6. The actuation position 805 can be shown as lagging behind the observed pedal travel distance value 803.

[0079] In this scenario, the operator intervenes in braking control by pressing the brake pedal between times t4 and t5. In one embodiment, the braking control system detects the operator's intervention by determining that the observed brake pedal travel sensor value 803 suddenly exhibits a large change (delta) within a predetermined time period. For example, t4 may correspond to planning period 1, and t5 may correspond to planning period 2. Between planning period 1 and planning period 2, when the delta value of the measured pedal travel distance is greater than a predetermined threshold, the control system can detect that the operator has intervened, for example, by pressing the brake pedal. Planning periods 1-2 may correspond to two consecutive planning periods (one planning period may be 10 ms or greater), or they may correspond to any two discontinuous planning periods. In one embodiment, the braking control system determines that the deviation between the observed brake pedal travel value and the original pedal travel sensor value is higher than a predetermined threshold (e.g., 0.1), and in response to determining that the deviation is higher than this predetermined threshold, detects the operator's intention to apply braking control.

[0080] In another embodiment, the brake control system detects operator intervention by determining that the observed brake pedal travel sensor value 803 minus the motor actuator value 805 is greater than a threshold within a defined time period. For example, at time = t4.5 (e.g., a planning cycle), the system can determine that the brake pedal travel sensor value is 7 and the motor actuator value is 2.8. Using the motor actuator value of 2.8, the system can determine a threshold of 2.7. The threshold can be determined using a mapping table, such as... Figure 13A Mapping table 1300 is used. Using a defined threshold, the system can determine that the observed brake pedal travel sensor value minus the motor actuator value is greater than the threshold, for example, 7 - 2.8 > 2.7. Therefore, the system detects that the operator has intervened. In this case, the operator intervention / intention can be robustly detected during the period when the control signal rises, for example, from time t0 to t6.

[0081] refer to Figure 13A Mapping table 1300 can reference historical data values ​​collected by ADV (e.g., ADV 101) without any operator intervention. Mapping table 1300 can correspond to data stored in... Figure 3A The mapping table 313 contains a specific target pedal travel distance. Historical data values ​​may include pedal travel distance value 1305 and actuation position value 1307. The threshold 1309 can be calculated as: pedal travel distance 1305 - actuation position value 1307 + a predetermined value (e.g., 0.5). In another embodiment, the threshold can be calculated using a formula where the pedal travel distance and actuation position value are represented as a linear curve of y = a*x + b, a quadratic curve of y = a*x^2 + b*x + c, or other curves. Figure 13B The graph shows the pedal travel distance value 1305, the actuation position value 1307, and the threshold value 1309.

[0082] Figure 9 This is a block diagram illustrating embodiment 900, which detects braking intent during brake holding according to one embodiment. Example 900 may correspond to... Figure 8 Example 800 in the example. Figure 9As shown, between time t1 and t2, the ADS requests brake hold, and brake hold is reflected by the requested brake pedal travel value 901 being approximately the same as the pedal travel value 903 observed by the sensor observer. In one embodiment, during brake hold, the brake control system can detect operator intervention by determining that the observed brake pedal travel sensor value 903 suddenly changes by a predetermined amount within a predetermined time period. For example, t1 may correspond to planning period 1 and t2 may correspond to planning period 2. When the observed change in pedal travel distance 903 exceeds a predetermined threshold (e.g., 0.5) between planning period 1 and planning period 2, the control system can detect that the operator has intervened, for example, by pressing the brake pedal. Planning periods 1-2 may correspond to two consecutive planning periods (each planning period being 100 ms) or any two discontinuous planning periods.

[0083] In another embodiment, the braking control system detects operator intervention by determining that the observed brake pedal travel sensor value 903 within a predetermined time period (e.g., 0.02 s) is greater than the requested brake pedal travel value 901 minus a predetermined threshold (e.g., 0.5). For example, at time t1, the system may determine that the requested brake pedal travel sensor value 901 is the target value, and the observed brake pedal travel sensor value 903 is approximately the target value. Therefore, the target > target - 0.5, and the system determines that the operator has intervened.

[0084] Figure 10 This is a block diagram illustrating Example 1000, which detects braking intent during brake release according to one embodiment. Example 1000 may correspond to... Figure 8 Example 800 in the example. Figure 10 As shown, for a brake release AD event, ADS requests the release of brake control. The requested control signal 1001 is characterized by a downhill curve, while the observed pedal travel sensor value 1003 and the measured actuation position sensor value 1005 follow the downhill curve of 1001.

[0085] In one embodiment, the brake control system detects operator intervention by determining that the observed brake pedal travel sensor value 1003 suddenly changes significantly (delta) within a predetermined time period. For example, t7 may correspond to planning period 1 and t8 may correspond to planning period 2. When the delta value of the measured pedal travel distance is greater than a predetermined threshold (e.g., 0.5) during the time between planning period 1 and planning period 2, the control system can detect that the operator has intervened, for example, by pressing the brake pedal. Planning periods 1-2 may correspond to two consecutive planning periods (each planning period being 100 ms) or any two discontinuous planning periods.

[0086] In another embodiment, the brake control system detects operator intervention by determining that the observed brake pedal travel sensor value 1003 minus the motor actuator value 1005 is greater than a threshold. For example, at time = t7.5 (planning cycle), the system can determine that the brake pedal travel sensor value is 5 and the motor actuator value is 2.4. Using the motor actuator value of 2.4, the system can infer and determine that the threshold is approximately 2.8. The threshold can be used... Figure 13A The mapping table 1300 is defined in the system. Using the defined threshold, the system can determine that the brake pedal travel sensor value minus the motor actuator value is greater than the threshold, for example, 5 - 2.4 > 2.8. Therefore, the system detects that the operator has intervened.

[0087] Figure 11 This is a block diagram illustrating Example 1100, which detects braking intent during a deceleration request having two or more braking control pressures according to one embodiment. Example 1100 may represent... Figure 8 Example 800 in the example. Figure 11 As shown, ADS requests deceleration at time t0, and the requested pedal travel value 1101 represents an uphill curve. The observed pedal travel value 1103 increases accordingly in line with the requested pedal travel value 1101. During the ascent, the operator depresses the brake control two or more times consecutively, indicating an intention to take over manual operation of the vehicle. In this scenario, the brake control system can detect operator intervention as follows.

[0088] In one embodiment, the brake control system detects operator intervention by determining that the observed brake pedal travel sensor value 1103 suddenly changes significantly (delta) within a predetermined time period, such as between t4 and t5, or between t5 and t9. For example, t4 may correspond to planning cycle 1, t5 to planning cycle 2, and t9 to planning cycle 3. When the delta value of the observed pedal travel distance is greater than a predetermined threshold between planning cycle 1 and planning cycle 2, or between planning cycle 2 and planning cycle 3, the control system can detect that the operator has intervened, for example, by pressing the brake pedal. Planning cycles 1-3 may correspond to three consecutive planning cycles (planning cycle of 100 ms), or they may correspond to any three discontinuous planning cycles. In one embodiment, two brake control engagements can be detected as a single operator intervention.

[0089] Figure 12 This is a block diagram illustrating example 1200 of detecting braking intent during a tilt deceleration request according to one embodiment. Example 1200 may represent Figure 9 Example 900 in the example. Figure 12As shown, the ADS requests a pedal travel value as a ramp curve via signal 1201. Since the AD request represents a ramp curve, the observed pedal travel value 1203 accordingly follows the requested curve 1201. In this scenario, operator intent can be detected similarly to that in Example 900.

[0090] For example, in one embodiment, the brake control system can detect operator intervention by determining that the observed brake pedal travel sensor value 1203 suddenly changes by a predetermined amount within a predetermined time period. For example, t4 may correspond to planning period 1, and t5 may correspond to planning period 2. When the observed change in pedal travel distance 1203 within the time between planning period 1 and planning period 2 is greater than a predetermined threshold (e.g., 0.5), the brake control system can detect that the operator has intervened, for example, by pressing the brake pedal. Planning periods 1-2 may correspond to two consecutive planning periods (each planning period being 100 ms), or they may correspond to any two discontinuous planning periods.

[0091] In another embodiment, the brake control system detects operator intervention by determining that the observed brake pedal travel sensor value 1203 is greater than the requested brake pedal travel value 1201 minus a predetermined threshold (e.g., 0.5). For example, at time t4, the system may determine that the requested brake pedal travel sensor value 1201 is equal to the target value, and the observed brake pedal travel sensor value 1203 is approximately the target value. Therefore, Target > Target - 0.5, and the system determines that the operator has intervened.

[0092] Figure 14 This is a flowchart illustrating a method for detecting an operator's braking intention according to one embodiment. Process 1400 can be executed by processing logic, which may include software, hardware, or a combination thereof. For example, process 1400 may be performed by… Figure 1 The braking intention module 308 executes.

[0093] In block 1401, the processing logic determines the brake pedal travel value based on the brake pedal travel sensor of the autonomous vehicle (ADV). In block 1403, the processing logic determines the brake actuation position based on the actuation sensor of the ADV.

[0094] In block 1405, the processing logic determines a first threshold based on the braking actuation position. Figure 13A (Threshold 1309). In block 1407, the processing logic determines that the deviation between the brake pedal travel value and the brake actuation position is higher than a first threshold. In block 1409, in response to determining that the deviation is higher than the first threshold, the processing logic detects the operator's intention to apply brake control. See also Figure 8 Example 800 in the example.

[0095] In one embodiment, in response to detecting an operator's intention to apply braking control, the processing logic cancels the autonomous driving event of the ADV's autonomous driving system to return the driving operation to the operator.

[0096] In one embodiment, the first threshold is a dynamic threshold proportional to the braking actuation position of the ADV, wherein the dynamic threshold is determined based on a mapping table, such as... Figure 13A Mapping table 1300.

[0097] In one embodiment, when the autonomous driving system of the ADV requests the ADV to decelerate, it is determined that the deviation between the brake pedal travel value and the brake actuation position is higher than a first threshold.

[0098] In one embodiment, the processing logic determines that the change in brake pedal travel value between a first planning cycle and a second planning cycle exceeds a predetermined threshold. In response to determining that the change in brake pedal travel value exceeds the predetermined threshold, the processing logic detects the operator's intention to apply brake control. See also... Figure 8 Example 800 and / or Figure 10 Example 1000.

[0099] In one embodiment, the processing logic determines that the change in brake pedal travel value between a second planning cycle and a third planning cycle exceeds a predetermined threshold. In response to determining that the change in brake pedal travel value exceeds the predetermined threshold (e.g., 0.3) between any of the first, second, or third planning cycles, the processing logic detects the operator's intention to apply brake control. See also Figure 11 Example 1100.

[0100] In one embodiment, the processing logic determines that the brake pedal travel value is higher than the brake pedal travel value requested by the ADV's autonomous driving system. In response to determining that the brake pedal travel value is higher than the requested brake pedal travel value, the processing logic detects the operator's intention to apply brake control. See also Figure 9 Example 900 and / or Figure 12 Example 1200.

[0101] In one embodiment, the actuation sensor is located in the feedback loop of the ADV's Autonomous Driving System (ADS). In one embodiment, the actuation sensor includes a sensor that captures the position of the actuation motor (motor 11) of the brake booster (booster 10). Figure 5 (Sensor 13 in the text). In one embodiment, the processing logic measures the signal from the brake pedal travel sensor and the signal from the actuation sensor of the electronic control unit (ECU) of the brake booster to determine the operator's intention to apply brake control.

[0102] Figure 15This is a flowchart illustrating a method for detecting an operator's braking intention according to another embodiment. Process 1500 can be executed by processing logic, which may include software, hardware, or a combination thereof. For example, process 1500 may be performed by… Figure 1 The braking intention module 308 in the middle is executed.

[0103] In block 1501, the processing logic determines the first and second brake pedal travel values ​​corresponding to the first and second planning cycles of the autonomous vehicle (ADV).

[0104] In block 1503, the processing logic determines the difference between the first and second brake pedal travel values. In block 1505, in response to determining that the difference between the first and second brake pedal travel values ​​is higher than a predetermined threshold, the processing logic detects the operator's intention to apply brake control. See also Figure 8 Example 800 in the example.

[0105] In one embodiment, in response to detecting an operator's intention to apply braking control, the processing logic further cancels the autonomous driving event of the ADV's autonomous driving system to return the driving operation to the operator.

[0106] In one embodiment, the processing logic determines the brake actuation position based on the ADV's actuation sensor. The processing logic determines that the deviation between the first brake pedal travel value and the brake actuation position is greater than a first threshold. In response to determining that the deviation is greater than the first threshold, the processing logic detects the operator's intention to apply brake control. See also... Figure 8 Example 800 in the example.

[0107] In one embodiment, the first threshold is a dynamic threshold proportional to the braking actuation position of the ADV, wherein the dynamic threshold is determined based on a mapping table.

[0108] In one embodiment, when the ADV's autonomous driving system has requested the ADV to decelerate, a delta is determined between the first and second brake pedal travel values. See also Figure 8 Example 800 in the example.

[0109] Note that some or all of the components shown and described above can be implemented in software, hardware, or a combination thereof. For example, these components can be implemented as software installed and stored in a permanent storage device, which can be loaded and executed in memory by a processor (not shown) to perform the processes or operations described throughout this application. Alternatively, these components can be implemented as executable code programmed or embedded in dedicated hardware such as integrated circuits (e.g., application-specific ICs or ASICs), digital signal processors (DSPs), or field-programmable gate arrays (FPGAs), accessible via corresponding drivers and / or operating systems from the application. Furthermore, these components can be implemented as specific hardware logic within a processor or processor core as part of an instruction set accessible via one or more specific instruction software components.

[0110] Some parts of the foregoing detailed description of algorithms and symbolic representations for operations on data bits within computer memory have already been presented. These algorithmic descriptions and representations are the most efficient way for those skilled in the art of data processing to communicate the substance of their work to others skilled in the art. Algorithms here and generally are considered to be self-consistent sequences of operations that lead to desired results. These operations are those that require physical manipulation of physical quantities.

[0111] However, it should be remembered that all these and similar terms are associated with appropriate physical quantities and are merely convenient notations applied to those quantities. Unless otherwise stated, it is obvious from the above discussion that it should be understood that throughout the specification, the use of terms such as those set forth in the appended claims refers to the actions and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (electronic) quantities in the registers and memories of the computer system into other data similarly represented as physical quantities in the computer system's memory or registers or other such information storage, transmission, or display devices.

[0112] Embodiments of this disclosure also relate to means for performing the operations described herein. Such a computer program is stored in a non-transitory computer-readable medium. Machine-readable media include any mechanism for storing information in a machine-readable (e.g., computer-readable) form. For example, machine-readable (e.g., computer-readable) media include machine-readable (e.g., computer-readable) storage media (e.g., read-only memory (“ROM”), random access memory (“RAM”), disk storage media, optical storage media, flash memory devices).

[0113] The processes or methods described in the foregoing figures can be performed by processing logic including 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 have been described above according to some sequential operations, it should be understood that some of the operations can be performed in different orders. Furthermore, some operations can be performed in parallel rather than sequentially.

[0114] The embodiments disclosed herein are not described with reference to any particular programming language. It will be understood that the teachings of the embodiments of this disclosure as described herein can be implemented using various programming languages.

[0115] In the foregoing description, embodiments of the present disclosure have been described with reference to specific exemplary embodiments. It will be apparent that various modifications may be made thereto without departing from the broader spirit and scope of the present disclosure as set forth in the appended claims. Therefore, the description and drawings should be considered illustrative rather than restrictive.

Claims

1. A method for detecting an operator's braking intention, comprising: Brake pedal travel value is determined based on brake pedal travel sensor of autonomous vehicle (ADV); The actuation sensor based on ADV determines the brake actuation position, wherein the actuation sensor includes a sensor that captures the position of the actuation motor of the brake booster; A first threshold is determined based on the braking actuation position; Determining that the deviation between the brake pedal travel value and the brake actuation position is higher than the first threshold; and In response to determining that the deviation is higher than the first threshold, an operator's intention to apply braking control is detected; The first threshold is a dynamic threshold proportional to the braking actuation position of the ADV, wherein the dynamic threshold is determined based on a mapping table.

2. The method of claim 1, further comprising, in response to detecting an operator's intention to apply braking control, canceling the autonomous driving event of the ADV's autonomous driving system to return the driving operation to the operator.

3. The method of claim 1, wherein when the autonomous driving system of the ADV has requested the ADV to decelerate, it is determined that the deviation between the brake pedal travel value and the brake actuation position is higher than the first threshold.

4. The method of claim 1, further comprising: It is determined that the change in the brake pedal travel value between the first planning period and the second planning period is higher than a predetermined threshold. as well as In response to determining that the change in the brake pedal travel value is higher than the predetermined threshold, the operator's intention to apply brake control is detected.

5. The method of claim 4, further comprising: It is determined that the change in the brake pedal travel value between the second and third planning cycles is higher than the predetermined threshold. as well as In response to determining that the change in the brake pedal travel value is higher than the predetermined threshold in any of the first planning cycle, the second planning cycle, or the third planning cycle, an operator's intention to apply brake control is detected.

6. The method of claim 1, further comprising: It is determined that the brake pedal travel value is higher than the brake pedal travel value requested by the autonomous driving system of the ADV; as well as In response to determining that the brake pedal travel value is higher than the requested brake pedal travel value, an operator's intention to apply brake control is detected.

7. The method of claim 6, wherein the actuation sensor is in the feedback loop of the autonomous driving system (ADS) of the ADV.

8. The method of claim 1, further comprising measuring signals from the brake pedal travel sensor and measuring signals from the actuation sensor of the brake booster to determine the operator's intention to apply brake control.

9. A non-transitory machine-readable medium storing instructions that, when executed by a processor, cause the processor to perform the operation of the method as described in any one of claims 1 to 8.

10. A data processing system, comprising: processor; as well as A memory coupled to a processor and storing instructions that, when executed by the processor, cause the processor to perform the operation of the method as described in any one of claims 1 to 8.

11. A computer program product comprising a computer program that, when executed by a processor, causes the processor to perform the operation of the method as described in any one of claims 1 to 8.

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