System and method for proactive lane assistance

Abstract

A proactive pedal algorithm is used to modify the accelerator pedal map so that deceleration upon release of the accelerator pedal matches the driver's expectations. By modifying the accelerator pedal map, the vehicle driver feels the vehicle resist moving when moving through dense scenes that potentially require high deceleration, and coast easily through scenes that require low deceleration. The accelerator pedal map is modified based on scene determinations that classify other remote vehicles as in-lane, adjacent lane, or oncoming.

Description

[Technical field]

[0001] The present disclosure relates to autonomous vehicle operation management and advanced driver assistance systems. [Background technology]

[0002] One-pedal functionality in vehicles allows drivers to drive without using a brake pedal. This is achieved by allowing the driver to regulate a relatively high rate of deceleration using only the accelerator pedal. However, a common complaint is that the vehicle slows down too much when the driver releases the accelerator pedal, such as when driving on a highway. This requires the driver to concentrate and carefully regulate the vehicle's speed. In some cases, the vehicle does not slow down enough when the driver releases the accelerator pedal, such as when a vehicle ahead begins to decelerate rapidly. This increases stress while driving. Thus, a system and method are needed to match the accelerator pedal's behavior with the driver's expectations. Summary of the Invention

[0003] Disclosed herein are aspects, features, elements, implementations, and embodiments of autonomous vehicle operation management and proactive lane assistance in autonomous driving.

[0004] One aspect of the disclosed embodiments is a method for use in a host vehicle. The method includes acquiring sensor data. The sensor data may be acquired to determine a scene associated with an operating environment of the host vehicle. The method includes calculating a deceleration estimate. The deceleration estimate may be based on the scene. The method includes calculating a feedback force. The feedback force may be based on the deceleration estimate. The method includes adjusting an accelerator pedal output (APO) to torque conversion based on the calculated feedback force. The APO to torque conversion may be at a ratio based on the scene.

[0005] One aspect of the disclosed embodiment is a host vehicle. The host vehicle may include one or more sensors. The one or more sensors may be configured to acquire data from an operating environment of the host vehicle. The host vehicle may include a processor configured to determine a scene based on the acquired data. The processor may be configured to calculate a deceleration estimate based on the scene. The processor may be configured to calculate a feedback force based on the deceleration estimate. The processor may be configured to adjust an APO to torque conversion based on the calculated feedback force. The APO to torque conversion may be at a ratio based on the scene.

[0006] These and other aspects, features, elements, implementations and embodiment variations of the methods, apparatus, procedures and algorithms disclosed herein are described in further detail below.

[0007] Various aspects of the methods and apparatus disclosed herein will become more apparent with reference to the examples provided in the following description and drawings. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 illustrates an example of a vehicle in which aspects, features, and elements disclosed herein may be implemented.

[0009] [Diagram 2] FIG. 1 illustrates an example of a portion of a vehicle traffic and communication system in which aspects, features, and elements disclosed herein may be implemented.

[0010] [Diagram 3] FIG. 1 illustrates an example of a proactive lane assistance system for use in a vehicle according to the present disclosure.

[0011] [Figure 4] FIG. 4 is a diagram of an example of a vehicle environment monitor of the proactive lane assistance system shown in FIG.

[0012] [Figure 5A] FIG. 1 is a block diagram of an example scene illustrating proactive lane assistance regions of interest in accordance with the present disclosure.

[0013] [Figure 5B] FIG. 13 is a block diagram of another example of a scene illustrating proactive lane assistance regions of interest in accordance with the present disclosure.

[0014] [Figure 6] FIG. 2 is a flow diagram illustrating an example of a proactive lane assistance method for use in a vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The proactive pedal algorithm may be used to modify the accelerator pedal map to generate more deceleration for the same accelerator pedal position and vehicle speed. Modifying the accelerator pedal map may give the vehicle driver a sense that the vehicle is resisting while approaching a lead vehicle, for example. The accelerator pedal map may be modified based on a scene determination, for example, to classify the vehicle into an in-lane, adjacent lane, or oncoming lane. A deceleration estimate may be generated based on the scene determination. The proactive lane assistance method and system disclosed herein may change the accelerator pedal range based on a lead vehicle, road attributes, static objects, or any combination thereof. Road attributes may include slopes, descents, curves, lane markings, stop lines, or any other road markings or characteristics.

[0016] The proactive lane assistance methods and systems disclosed herein may improve driver enjoyment. For example, the accelerator pedal range may be adjusted to match the driver's expectations so that in open and free-moving conditions, the driver can relax and take his / her foot off the accelerator as the vehicle coasts and cruises as expected. For example, in traffic jams or places requiring higher speed adjustments, such as intersections and parking lots, the vehicle may be configured to slow down sufficiently when the driver releases the accelerator pedal. The methods and systems disclosed herein may use machine learning methods for continuous scene determination.

[0017] Although described herein with reference to an autonomous vehicle, the methods and apparatus described herein may be implemented in any vehicle capable of autonomous or semi-autonomous operation. Although described herein with reference to a vehicular traffic network, the methods and apparatus described herein may include operation of an autonomous vehicle in any area navigable by a vehicle.

[0018] FIG. 1 illustrates an example of a vehicle in which aspects, features, and elements disclosed herein may be implemented. As illustrated, the vehicle 1000 includes a chassis 1100, a powertrain 1200, a controller 1300, and wheels 1400. For simplicity, the vehicle 1000 is shown to include four wheels 1400, but any other propulsion device, such as one or more propellers or treads, may be used. In FIG. 1, lines interconnecting elements, such as the powertrain 1200, the controller 1300, and the wheels 1400, indicate that information, such as data or control signals, forces, such as power or torque, or both information and power, may be transferred between the elements. For example, the controller 1300 may receive power from the powertrain 1200 and communicate with the powertrain 1200, the wheels 1400, or both, to control the vehicle 1000, which may include accelerating, decelerating, steering, or otherwise controlling the vehicle 1000.

[0019] As shown, the powertrain 1200 includes a power source 1210, a transmission 1220, a steering system 1230, and an actuator 1240. Other elements or combinations of elements of the powertrain may be included, such as suspension, drive shafts, axles, or an exhaust system. Although shown separately, wheels 1400 may be included in the powertrain 1200.

[0020] The power source 1210 may include an engine, a battery, or a combination thereof. The power source 1210 may be any device or combination of devices that operates to provide energy, such as electrical energy, thermal energy, or kinetic energy. For example, the power source 1210 may include an engine, such as an internal combustion engine, an electric motor, or a combination of an internal combustion engine and an electric motor, and may operate to provide kinetic energy as motive power to one or more wheels 1210. The power source 1210 may include a potential energy device, such as one or more dry batteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), solar cells, fuel cells, or any other device capable of providing energy.

[0021] The transmission 1220 may receive energy, such as kinetic energy, from the power source 1210 and may send energy to the wheels 1400 to provide motive power. The transmission 1220 may be controlled by the controller 1300, the actuator 1240, or both. The steering device 1230 may be controlled by the controller 1300, the actuator 1240, or both and may control the wheels 1400 to steer the vehicle. The actuator 1240 may receive signals from the controller 1300 and may operate or control the power source 1210, the transmission 1220, the steering device 1230, or any combination thereof to operate the vehicle 1000.

[0022] As shown, the controller 1300 may include a positioning device 1310, an electronic communication device 1320, a processor 1330, a memory 1340, a user interface 1350, a sensor 1360, an electronic communication interface 1370, or any combination thereof. Although shown as a single device, any one or more elements of the controller 1300 may be incorporated into any number of separate physical devices. For example, the user interface 1350 and the processor 1330 may be incorporated into a first physical device and the memory 1340 may be incorporated into a second physical device. Although not shown in FIG. 1, the controller 1300 may include a power source 1210, such as a battery. Although shown as separate elements, the positioning device 1310, the electronic communication device 1320, the processor 1330, the memory 1340, the user interface 1350, the sensor 1360, the electronic communication interface 1370, or any combination thereof may be incorporated into one or more electronic devices, circuits, or chips.

[0023] The processor 1330 may include any device or combination of devices now existing or later developed capable of manipulating or processing signals or other information, including optical processors, quantum processors, molecular processors, or combinations thereof. For example, the processor 1330 may include one or more special purpose processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more integrated circuits, one or more application specific integrated circuits, one or more field programmable gate arrays, one or more programmable logic arrays, one or more programmable logic controllers, one or more state machines, or any combination thereof. The processor 1330 may be operatively coupled to the positioning device 1310, the memory 1340, the electronic communication interface 1370, the electronic communication device 1320, the user interface 1350, the sensor 1360, the powertrain 1200, or any combination thereof. For example, the processor may be operatively coupled to the memory 1340 via a communication bus 1380.

[0024] Memory 1340 may include any tangible, non-transitory computer usable or computer readable medium capable of, for example, holding, storing, transmitting or carrying machine-readable instructions or any information associated therewith for use by or in connection with processor 1330. Memory 1340 may be, for example, one or more solid-state drives, one or more memory cards, one or more removable media, one or more read-only memories, one or more random access memories, one or more disks including hard disks, floppy disks, optical disks, magnetic or optical cards, or any type of non-transitory medium suitable for storing electronic information, or any combination thereof.

[0025] The communication interface 1370 may be a wireless antenna as shown, a wired communication port, an optical communication port, or any other wired or wireless device capable of interfacing with the wired or wireless electronic communication medium 1500. Although Figure 1 illustrates the communication interface 1370 communicating over a single communication link, the communication interface may be configured to communicate over multiple communication links. Although Figure 1 illustrates a single communication interface 1370, the vehicle may include any number of communication interfaces.

[0026] The communication device 1320 may be configured to transmit or receive signals over a wired or wireless electronic communication medium 1500, such as via a communication interface 1370. Although not explicitly shown in FIG. 1, the communication device 1320 may be configured to transmit, receive, or both over any wired or wireless communication medium, such as radio frequency (RF), ultraviolet (UV), visible light, optical fiber, wired line, or a combination thereof. Although FIG. 1 shows a single communication device 1320 and a single communication interface 1370, any number of communication devices and any number of communication interfaces may be used. In some embodiments, the communication device 1320 may include a Dedicated Short Range Communications (DSRC) device, an On-Board Unit (OBU), or a combination thereof.

[0027] The positioning device 1310 may determine geographic information such as the longitude, latitude, altitude, heading, or speed of the vehicle 1000. For example, the positioning device may include a Global Positioning System (GPS) device, such as a Wide Area Augmentation System (WAAS)-enabled National Marine-Electronics Association (NMEA) device, a radio triangulation device, or a combination thereof. The positioning device 1310 may be used to obtain information representing, for example, the current heading of the vehicle 1000, the current location of the vehicle 1000 in two or three dimensions, the current angular heading of the vehicle 1000, or a combination thereof.

[0028] The user interface 1350 may include any device capable of interfacing with a person, such as a virtual or physical keypad, a touchpad, a display, a touch display, a heads-up display, a virtual display, an augmented reality display, a tactile display, a feature tracker such as an eye-tracking device, a speaker, a microphone, a video camera, a sensor, a printer, or any combination thereof. The user interface 1350 may be operatively coupled to the processor 1330 as shown, or to any other element of the controller 1300. Although shown as a single device, the user interface 1350 may include one or more physical devices. For example, the user interface 1350 may include a voice interface for voice communication with the person, and a touch display for visual and touch-based communication with the person. The user interface 1350 may include multiple displays, such as multiple physically separate devices, multiple defined portions of a single physical device, or a combination thereof.

[0029] Sensors 1360 may include one or more sensors, such as an array of sensors operable to provide information that can be used to control the vehicle. Sensors 1360 may provide information regarding the current operating characteristics of vehicle 1000. Sensors 1360 may include, for example, speed sensors, acceleration sensors, steering angle sensors, traction-related sensors, brake-related sensors, steering wheel position sensors, eye-tracking sensors, seating position sensors, or any sensor or combination of sensors operable to report information regarding some aspect of the current dynamic situation of vehicle 1000.

[0030] Sensors 1360 may include one or more sensors operable to obtain information about the physical environment surrounding vehicle 1000. For example, one or more sensors may detect road features and shapes, such as lanes, and obstacles, such as fixed obstacles, vehicles, and pedestrians. Sensors 1360 may be or include one or more video cameras, laser sensing systems, infrared sensing systems, acoustic sensing systems, or any other suitable type of on-board environmental sensing device or combination of devices, now known or later developed. In some embodiments, sensors 1360 and positioning device 1310 may be a combined device.

[0031] Although not separately shown, the vehicle 1000 may include a trajectory controller. For example, the controller 1300 may include a trajectory controller. The trajectory controller may be operable to obtain information describing the current state of the vehicle 1000 and a planned route for the vehicle 1000, and to determine and optimize a trajectory for the vehicle 1000 based on this information. In some embodiments, the trajectory controller may output signals operable to control the vehicle 1000 such that the vehicle 1000 follows a trajectory determined by the trajectory controller. For example, the output of the trajectory controller may be an optimized trajectory that may be provided to the powertrain 1200, the wheels 1400, or both. In some embodiments, the optimized trajectory may be a control input, such as a set of steering angles, each steering angle corresponding to one time or position. In some embodiments, the optimized trajectory may be one or more paths, lines, curves, or combinations thereof.

[0032] One or more of the wheels 1400 may be steered wheels that may be pivoted to a steering angle under the control of the steering device 1230, propelled wheels that may be given a torque to propel the vehicle 1000 under the control of the transmission 1220, or steered and propelled wheels that may both steer and propel the vehicle 1000.

[0033] Although not shown in FIG. 1, the vehicle may include devices or elements not shown in FIG. 1, such as an enclosure, a Bluetooth® module, a frequency modulation (FM) radio device, a near field communication (NFC) module, a liquid crystal display (LCD) display device, an organic light emitting diode (OLED) display device, a speaker, or any combination thereof.

[0034] Vehicle 1000 may be an autonomous vehicle that is autonomously controlled without direct human intervention to navigate a portion of a transportation network. Although not separately shown in FIG. 1, the autonomous vehicle may include an autonomous vehicle controller that may perform routing, navigation, and control of the autonomous vehicle. The autonomous vehicle controller may be integrated with another device of the vehicle. For example, controller 1300 may include an autonomous vehicle controller.

[0035] The autonomous vehicle controller may control or operate the vehicle 1000 to move through a portion of the vehicle traffic network according to the current vehicle operation parameters. The autonomous vehicle controller may control or operate the vehicle 1000 to perform a defined operation or maneuver, such as parking the vehicle. The autonomous vehicle controller may generate a travel route from a starting point, such as the current location of the vehicle 1000, to a destination based on vehicle information, environmental information, vehicle traffic network data representing the vehicle traffic network, or a combination thereof, and may control or operate the vehicle 1000 to move through the vehicle traffic network according to the route. For example, the autonomous vehicle controller may output the travel route to a trajectory controller, and the trajectory controller may operate the vehicle 1000 to move from the starting point to the destination using the generated route.

[0036] Figure 2 is an example diagram of a portion of a vehicle traffic and communication system 2000 in which aspects, features, and elements disclosed herein may be implemented. The vehicle traffic and communication system 2000 may include one or more vehicles 2100 / 2110, such as the vehicle 1000 shown in Figure 1, which may travel in one or more portions of one or more vehicle traffic networks 2200 and may communicate via one or more electronic communication networks 2300. Although not explicitly shown in Figure 2, the vehicles may travel in areas not explicitly or completely included in the vehicle traffic network, such as off-road areas.

[0037] The electronic communications network 2300 may be, for example, a multiple access system and may provide communications, such as voice communications, data communications, video communications, messaging communications, or combinations thereof, between the vehicles 2100 / 2110 and one or more communications devices 2400. For example, the vehicles 2100 / 2110 may receive information, such as information describing the vehicle traffic network 2200, from the communications devices 2400 via the network 2300.

[0038] In some embodiments, the vehicles 2100 / 2110 may communicate via a wired communication link (not shown), a wireless communication link 2310 / 2320 / 2370, or any combination of any number of wired or wireless communication links. For example, as shown, the vehicles 2110 / 2110 may communicate via a terrestrial wireless communication link 2310, via a non-terrestrial wireless communication link 2320, or via a combination thereof. The terrestrial wireless communication link 2310 may include an Ethernet link, a serial link, a Bluetooth link, an infrared (IR) link, an ultraviolet (UV) link, or any link capable of providing electronic communications.

[0039] A vehicle 2100 / 2110 may communicate with another vehicle 2100 / 2110. For example, a host or target vehicle (HV) 2100 may receive one or more autonomous vehicle-to-vehicle messages, such as a basic safety message (BSM), from a remote or target vehicle (RV) 2110 via a direct communication link 2370 or via a network 2300. For example, a remote vehicle 2110 may broadcast a message to a host vehicle within a defined broadcast range, such as 300 meters. In some embodiments, the host vehicle 2100 may receive the message via a third party, such as a signal repeater (not shown) or another remote vehicle (not shown). A vehicle 2100 / 2110 may transmit one or more autonomous vehicle-to-vehicle messages periodically based on a defined interval, such as 100 milliseconds.

[0040] The automatic vehicle-to-vehicle messages may include vehicle identification information, geospatial state information such as longitude, latitude or altitude information, geospatial position accuracy information, motion state information such as vehicle acceleration information, yaw rate information, speed information, vehicle heading information, braking system state information, throttle information, steering angle information or vehicle path information, or vehicle operating state information such as vehicle size information, headlight state information, turn signal information, wiper state information, transmission information or any other information or combination of information related to the transmitting vehicle state. For example, the transmission state information may indicate whether the transmission of the transmitting vehicle is in neutral, parked, forward or reverse.

[0041] The vehicle 2100 may communicate with the communication network 2300 via an access point 2330. The access point 2330, which may include a computing device, may be configured to communicate with the vehicle 2100, the communication network 2300, one or more communication devices 2400, or combinations thereof, via wireless or wired communication links 2310 / 2340. For example, the access point 2330 may be a base station, a base transceiver station (BTS), a Node-B, an enhanced Node-B (eNode-B), a Home Node-B (HNode-B), a wireless router, a wired router, a hub, a relay, a switch, or any similar wired or wireless device. Although shown as a single device in FIG. 2, the access point may include any number of interconnection elements.

[0042] The vehicle 2100 may communicate with the communications network 2300 via a satellite 2350 or other non-terrestrial communications device. The satellite 2350, which may include a computing device, may be configured to communicate with the vehicle 2100, the communications network 2300, one or more communications devices 2400, or combinations thereof, via one or more communications links 2320 / 2360. Although shown as a single device in FIG. 2, the satellite may include any number of interconnecting elements.

[0043] The electronic communications network 2300 may be any type of network configured to provide voice, data, or any other type of electronic communications. For example, the electronic communications network 2300 may include a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a mobile or cellular telephone network, the Internet, or any other electronic communications system. The electronic communications network 2300 may use communications protocols such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Internet Protocol (IP), Real-time Transport Protocol (RTP), Hypertext Transport Protocol (HTTP), or combinations thereof. Although shown as a single device in FIG. 2, the electronic communications network may include any number of interconnected elements.

[0044] The vehicle 2100 may identify a portion or state of the vehicle network 2200. For example, the vehicle 2100 may include one or more on-board sensors 2105, such as the sensors 1360 shown in FIG. 1, which may include a speed sensor, a wheel speed sensor, a camera, a gyroscope, an optical sensor, a laser sensor, a radar sensor, an acoustic sensor, or any other sensor or device or combination thereof capable of determining or identifying a portion or state of the vehicle network 2200. The sensor data may include lane data, remote vehicle position data, or both.

[0045] The vehicle 2100 may travel through one or more portions of one or more vehicle traffic networks 2200 using information communicated via a network 2300, such as information representing the traffic network 2200, one or more onboard sensors 2105, or a combination thereof.

[0046] For simplicity, Figure 2 shows two vehicles 2100, 2110, one vehicle traffic network 2200, one electronic communication network 2300, and one communication network 2400, although any number of networks or computer devices may be used. The vehicle traffic and communication system 2000 may include devices, units, or elements not shown in Figure 2. Although the vehicle 2100 is shown as a single device, the vehicle may include any number of interconnected elements.

[0047] Although the vehicle 2100 is shown communicating with the communication device 2400 via the network 2300, the vehicle 2100 may communicate with the communication device 2400 via any number of direct or indirect communication links. For example, the vehicle 2100 may communicate with the communication device 2400 via a direct communication link, such as a Bluetooth® communication link.

[0048] In some embodiments, a vehicle 2100 / 2210 may be associated with an entity 2500 / 2510, such as a driver, operator, or owner of the vehicle. In some embodiments, an entity 2500 / 2510 associated with a vehicle 2100 / 2110 may be associated with one or more personal electronic devices 2502 / 2504 / 2512 / 2514, such as a smartphone 2502 / 2512 or a computer 2504 / 2514. In some embodiments, a personal electronic device 2502 / 2504 / 2512 / 2514 may communicate with a corresponding vehicle 2100 / 2110 via a direct or indirect communication link. Although one entity 2500 / 2510 is shown as associated with the vehicle 2100 / 2110 in FIG. 2, any number of vehicles may be associated with an entity, and any number of entities may be associated with a vehicle.

[0049] 3 is a diagram illustrating an example of a proactive lane assistance system 3000 for use in a vehicle according to the present disclosure. The proactive lane assistance system 3000 includes a processor 3010, such as the processor 1330 shown in FIG. 1, a memory 3020, such as the memory 1340 shown in FIG. 1, and one or more sensors 3030, such as the sensor 1360 shown in FIG. 1.

[0050] The processor 3010 includes a vehicle environment monitor 3040 and a vehicle controller 3050. The vehicle environment monitor 3040 may correlate, associate, or otherwise process the operating environment data to determine a scene. Determining the scene may include identifying, tracking, or predicting the actions of one or more remote vehicles in the host vehicle's operating environment, such as information indicative of a remote vehicle that is slow or stationary along a predicted path of the host vehicle, corresponding geo-spatially to lane activity, identifying one or more aspects of the host vehicle's operating environment, such as the shape of a vehicular network in the host vehicle's operating environment, or a combination thereof. For example, the vehicle environment monitor 3040 may receive information, such as sensor data, from one or more sensors 3030 that may correspond to one or more remote vehicles in the host vehicle's operating environment, one or more aspects of the host vehicle's operating environment in the host vehicle's operating environment, or a combination thereof, corresponding geo-spatially to a scene, such as related to braking, acceleration, steering, or any combination thereof. The vehicle environment monitor 3040 may associate the sensor data with one or more identified remote vehicles in the host vehicle's operating environment, one or more aspects of the host vehicle's operating environment, or combinations thereof, in geospatially corresponding correspondence with the vehicle operation, which may include identifying a current or predicted heading, a path such as a predicted path, a current or predicted speed, a current or predicted acceleration, or combinations thereof, for one or more of each identified remote vehicle. The vehicle environment monitor 3040 may output the identified, associated, or generated scene information to or for access by the vehicle controller 3050. The scene information may classify the vehicle as in-lane, adjacent lane, oncoming lane, or other classification. An in-lane vehicle may be classified as a lead vehicle that the host vehicle has identified it is following. An adjacent lane vehicle may be classified as an adjacent vehicle that is in an adjacent lane. An adjacent vehicle may be reclassified as a lead vehicle after or while the host vehicle executes a lane change into the adjacent lane. An oncoming vehicle is a vehicle traveling in the direction toward the host vehicle and may be in the same lane as the host vehicle or an adjacent lane.

[0051] The memory 3020 includes one or more pedal maps 3060. The pedal maps 3060 may be referred to as accelerator maps and may be associated with a driving mode such as a normal mode, a regenerative mode, or a comfort mode. For example, a regenerative mode may provide strong deceleration (i.e., active braking) when the accelerator pedal is released, and a comfort mode may provide minimal deceleration to provide a gliding experience when the accelerator pedal is released. A normal mode may be a mix of the regenerative and comfort modes, where moderate deceleration is provided. Each pedal map may be a representation of how to convert a driver's accelerator pedal output (APO) into a driver's torque demand. The pedal maps may be represented as curves of torque versus speed and APO, and may be used to estimate a driver's torque or acceleration demand based on the driving mode, host vehicle speed, APO, or any combination thereof.

[0052] The vehicle controller 3050 includes a proactive assistance controller 3070 and is configured to receive scene information from the vehicle environment monitor 3040. The proactive assistance controller 3070 is configured to modify a pedal map from the memory 3020 based on the scene information. The dynamically modified pedal map may be used to adjust the maximum deceleration when the accelerator pedal is released based on an estimate of potential deceleration requirements in the near future (i.e., within the next 10 seconds). The estimate of the maximum deceleration may be based on deceleration estimates for the lead vehicle, road attributes, static objects, or any combination thereof. The deceleration estimate for the lead vehicle may be based on a function of the current speed of the host vehicle (v1), the target speed of the host vehicle (v2), and the time (t) corresponding to the speed of the lead vehicle, and may be expressed as follows: [Formula 1]

number

[0053] The lead vehicle deceleration estimate may be a dynamic estimate based on a function of the lead vehicle's relative distance from the host vehicle, the lead vehicle's relative speed, and the lead vehicle's relative acceleration.

[0054] The road attribute deceleration estimate may be based on a function of the current speed of the host vehicle (v1), the target speed of the host vehicle (v2), and the distance to the target location (d), and may be expressed as follows: [Formula 2]

number

[0055] The stationary object deceleration estimate may be based on the host vehicle's current speed (v1), the stationary object's target speed (v2), where v2=0, and the distance to the stationary object (d), and may be expressed as follows: [Formula 3]

number

[0056] A filter 3075 may be applied to the output of the proactive assistance controller 3070. The filter 3075 may output a proactive assistance request 3080. In one example, the proactive assistance request 3080 may be a torque request that is subtracted from a nominal torque request in a selected driving mode to adjust the maximum deceleration possible when the accelerator pedal is released. The filter 3075 may be a rate limiter used to smoothly change the proactive assistance request 3080 to adjust the maximum deceleration when the accelerator pedal is released. The proactive assistance request 3080 may be ramped such that an initial adjustment value is incremented at a rate based on the determined scene until a maximum adjustment value is reached. The rate of the adjustment value relative to the estimate of the maximum deceleration may be adjusted based on the curve. For example, the estimate of the maximum deceleration may be adjusted at a first rate when entering the determined scene, and the estimate of the maximum deceleration may be adjusted at a second rate when exiting the determined scene. For example, the first rate may be about six times the second rate such that the accelerator feedback force is adjusted rapidly to ramp into the determined scene when entering the determined scene and adjusted at a slower rate to exit the determined scene. Exiting the determined scene in this case may include returning to normal driving conditions when the determined scene changes, such as when the lead vehicle exits a highway, or when some other change to the scene occurs.

[0057] 4 is a diagram illustrating an example of a vehicle environment monitor 4000, such as the vehicle environment monitor 3040 shown in FIG. 3. The vehicle environment monitor 4000 is configured to obtain sensor data 4010, for example, from one or more sensors 3030 shown in FIG. 3. The sensor data 4010 may include a current speed of the host vehicle, a yaw rate of the host vehicle, a steering angle of the host vehicle, host vehicle acceleration data, host vehicle position data, a distance between the host vehicle and a remote vehicle, a current speed of the remote vehicle, remote vehicle acceleration data, remote vehicle position data, data related to road attributes such as a width of a lane in which the host vehicle is traveling, data related to stationary objects, a distance between the host vehicle and a stationary object, or any combination thereof. A lead vehicle may be a remote vehicle traveling in the same lane as the host vehicle that is identified as a vehicle for the host vehicle to follow.

[0058] The vehicle environment monitor 4000 includes a region of interest estimator 4020. The region of interest estimator 4020 is configured to determine a region of interest estimate based on the sensor data 4010. The region of interest may be based on the speed of the host vehicle, the steering angle of the host vehicle, the yaw rate of the host vehicle, the longitudinal distance ahead of the host vehicle, the lateral distance to the left or right of the vehicle based on the width of the lane or the width of the leading or adjacent vehicle, or any combination thereof. The longitudinal and lateral distances are based on X and Y coordinates relative to the host vehicle, where X is the distance the host vehicle is going straight ahead and Y is the distance to the left or right of the host vehicle. In one example, if the host vehicle is moving at 30 meters per second with a yaw rate of zero, the host vehicle is expected to move 60 meters longitudinally and 0 meters laterally in 2 seconds. If the yaw rate is not zero, the expected longitudinal travel distance decreases as the host vehicle follows a curve, and the expected lateral travel distance increases, positive or negative, based on the yaw rate sign. The steering angle of the host vehicle may be related to the yaw rate of the host vehicle.

[0059] The vehicle environment monitor 4000 includes a host vehicle tracking tool 4030. The host vehicle tracking tool 4030 is configured to determine host vehicle tracking based on the sensor data 4010. In one example, a host vehicle sensor may report observations of other remote vehicles in a vehicle frame at time t. Thus, a set of observations of other remote vehicles are all in different frames (e.g., tn, t-n+1,..., t-1, t). To compare the set of observations to a region of interest in the vehicle frame at time t, each observation is transformed from (ti) to (t). Thus, each vehicle frame may be tracked.

[0060] The vehicle environment monitor 4000 includes a remote vehicle tracking tool 4040. The remote vehicle tracking tool 4040 is configured to receive the output of the host vehicle tracking tool 4030 and track one or more remote vehicles. The remote vehicle tracking tool 4040 is configured to calculate tracking data associated with the remote vehicles 4050A to 4050N. The tracking data for each remote vehicle may be calculated based on a remote vehicle identification (ID), a lateral distance between each remote vehicle and the host vehicle, a longitudinal distance between each remote vehicle and the host vehicle, a relative speed of each remote vehicle, or any combination thereof.

[0061] The vehicle environment monitor 4000 includes an interaction estimator 4060. The interaction estimator 4060 is configured to receive the region of interest estimates from the region of interest estimator 4020 and an output from the remote vehicle tracking tool 4040. The interaction estimator 4060 is configured to determine an interaction type 4070A to 4070N associated with each remote vehicle. The interaction type may include, for example, whether each remote vehicle is a lead vehicle, a vehicle to the left of the host vehicle, a vehicle to the right of the host vehicle, an oncoming vehicle, or another type of interaction. The interaction estimator 4060 may be configured to determine a scene based on the region of interest estimates, the output from the remote vehicle tracking tool 4040, one or more determined interaction types, or any combination thereof. The interaction estimator 4060 may be configured to determine a probability value for the scene determination, the lead vehicle determination, or both, where the probability value for the scene determination is a confidence value associated with the confidence level the system has in that the scene determination is correct, and the probability value for the lead vehicle determination is a confidence value associated with the confidence level the system has in that the lead vehicle determination is correct. The interaction estimator 4060 may be configured to output data 4080 associated with the determined scene, for example, to the vehicle controller 3050 shown in FIG. 3. In one example, once the trajectory of the remote vehicle is transformed into a vehicle frame at time t, the trajectory may be directly compared against the region of interest using linear regression. The results of the linear regression are used to determine a suitable match (i.e., a remote vehicle in the same lane), deviation (i.e., a remote vehicle moving away from the region of interest), merge (i.e., a remote vehicle entering the region of interest), or lateral offset (i.e., a remote vehicle traveling parallel to the region of interest).

[0062] 5A is a block diagram of an example of a scene 5000 showing a proactive lane assistance region of interest according to the present disclosure. FIG. 5A shows a host vehicle 5010 and a remote vehicle 5020 traveling in the same direction on a multi-lane road, such as a highway. In this example, the host vehicle 5010 and the remote vehicle 5020 are traveling in the same lane of the multi-lane road. The points shown between the host vehicle 5010 and the remote vehicle 5020 are points along which the vehicle 5010 is predicted to travel.

[0063] FIG. 5A shows a region of interest 5030, which is an estimated driving area of ​​the host vehicle 5010. The region of interest may be based on the speed of the host vehicle 5010, the steering angle of the host vehicle 5010, the yaw rate of the host vehicle 5010, the straight longitudinal distance of the host vehicle 5010, the lateral distance to the left or right of the host vehicle 5010 based on the width of the lane or the width of the leading or adjacent vehicle, or any combination thereof. The longitudinal and lateral distances are based on the X and Y coordinates relative to the host vehicle, where X is the straight longitudinal distance of the host vehicle 5010 and Y is the distance to the left or right of the host vehicle. In one example, if the host vehicle 5010 is moving at 30 meters per second with a yaw rate of 0, the host vehicle 5010 is expected to move 60 meters longitudinally and 0 meters laterally in 2 seconds. If the yaw rate is not 0, the expected longitudinal travel distance decreases as the host vehicle follows a curve, and the expected lateral travel distance increases, positive or negative, based on the yaw rate sign. The steering angle of the host vehicle 5010 may be related to the yaw rate of the host vehicle 5010. The region of interest 5030 may be compared to the trajectory of the remote vehicle 5020 to determine whether the remote vehicle 5020 is an in-lane vehicle, an adjacent lane vehicle, an oncoming vehicle, or other vehicle.

[0064] 5B is another block diagram of a scene 5100 showing a proactive lane assistance region of interest according to the present disclosure. FIG. 5B shows a host vehicle 5110 and a remote vehicle 5120 traveling in the same direction on a multi-lane road, such as a highway. In this example, the host vehicle 5110 and the remote vehicle 5120 are traveling in the same lane of the multi-lane road. The points shown between the host vehicle 5110 and the remote vehicle 5120, and points ahead of the remote vehicle 5120, are points where the vehicle 5110 is predicted to be on its way to the current location of the remote vehicle 5120.

[0065] FIG. 5B shows a region of interest 5130, which is an estimated driving area of ​​the host vehicle 5110. The region of interest may be based on the speed of the host vehicle 5110, the steering angle of the host vehicle 5110, the yaw rate of the host vehicle 5110, the straight longitudinal distance of the host vehicle 5110, the lateral distance to the left or right of the host vehicle 5110 based on the width of the lane or the width of the leading or adjacent vehicle, or any combination thereof. The longitudinal and lateral distances are based on X and Y coordinates relative to the host vehicle, where X is the straight longitudinal distance of the host vehicle 5110 and Y is the distance to the left or right of the host vehicle. In one example, if the host vehicle 5110 is moving at 30 meters per second with a yaw rate of 0, the host vehicle 5110 is expected to move 60 meters longitudinally and 0 meters laterally in 2 seconds. If the yaw rate is not 0, the expected longitudinal travel distance decreases as the host vehicle follows a curve, and the expected lateral travel distance increases, positive or negative, based on the yaw rate sign. The steering angle of the host vehicle 5110 may be related to the yaw rate of the host vehicle 5110. The region of interest 5130 may be compared to the trajectory of the remote vehicle 5120 to determine whether the remote vehicle 5120 is an in-lane vehicle, an adjacent lane vehicle, an oncoming vehicle, or other vehicle.

[0066] The example shown in Figures 5A and 5B assumes that the host vehicles 5010, 5110 are traveling at the same speed and that the region of interest should have the same length, where region of interest length = host vehicle speed x look-ahead time. A typical look-ahead time is 6-10 seconds. As a result, both remote vehicles 5020, 5120 are displayed as being contained within the region of interest. In other words, the region of interest may be calculated independently of the location of other road users. In the example shown in Figures 5A and 5B, the remote vehicle 5020 is farther away from the host vehicle 5010 than the remote vehicle 5120 is from the host vehicle 5110, so the impact on the proactive assistance request for the host vehicle 5010 is reduced compared to the proactive assistance request for the host vehicle 5110.

[0067] 6 is a flow diagram of an example of a proactive lane assistance method 6000 for use in a vehicle, such as the host vehicle 2100 shown in FIG. 2, according to an embodiment of the disclosure. The proactive lane assistance method 6000 may be performed by the proactive lane assistance system 3000 shown in FIG.

[0068] The proactive lane assistance method 6000 includes acquiring sensor data at step 6010. The sensor data may be acquired from one or more sensors, such as the one or more sensors 3030 shown in Figure 3. The sensor data may include a current speed of the host vehicle, a yaw rate of the host vehicle, a steering angle of the host vehicle, host vehicle acceleration data, host vehicle position data, a distance between the host vehicle and the remote vehicle, a current speed of the remote vehicle, remote vehicle acceleration data, remote vehicle position data, data related to road attributes such as a width of the lane in which the host vehicle is traveling, data related to stationary objects, a distance between the host vehicle and the stationary objects, or any combination thereof.

[0069] The sensor data may be correlated, associated, or otherwise processed to determine a scene associated with the host vehicle's operating environment. Determining the scene may include identifying, tracking, or predicting actions of one or more remote vehicles in the host vehicle's operating environment, such as information indicative of a slow or stationary remote vehicle along a predicted path of the host vehicle, corresponding geo-spatially to lane activity, identifying one or more aspects of the host vehicle's operating environment, such as the shape of a vehicular network in the host vehicle's operating environment, or a combination thereof. For example, the sensor data may correspond geo-spatially to a scene associated with braking, acceleration, steering, or any combination thereof, corresponding to one or more remote vehicles in the host vehicle's operating environment, one or more aspects of the host vehicle's operating environment in the host vehicle's operating environment, or a combination thereof, for example. The sensor data may be associated with one or more identified remote vehicles in the host vehicle's operating environment, one or more aspects of the host vehicle's operating environment, or combinations thereof, in geospatially corresponding manner to the vehicle operation, which may include identifying a current or predicted heading, a path such as a predicted path, a current or predicted speed, a current or predicted acceleration, or combinations thereof, for one or more of each identified remote vehicle. The scene information may classify vehicles as in-lane, adjacent lane, oncoming lane, or other classification. An in-lane vehicle may be classified as a lead vehicle that the host vehicle has identified to follow. An adjacent lane vehicle may be classified as an adjacent vehicle that is in an adjacent lane. An adjacent vehicle may be reclassified as a lead vehicle after or while the host vehicle executes a lane change to the adjacent lane. An oncoming vehicle is a vehicle traveling in a direction toward the host vehicle and may be in the same lane or an adjacent lane of the host vehicle.

[0070] The proactive lane assistance method 6000 includes calculating a deceleration estimate based on the scene at step 6020. The deceleration estimate may be a lead vehicle deceleration estimate. The lead vehicle deceleration estimate may be based on a function of a current speed of the host vehicle, a target speed of the host vehicle, and a time corresponding to the speed of the lead vehicle. The lead vehicle deceleration estimate may be a dynamic estimate based on a function of a relative distance of the lead vehicle from the host vehicle, a relative speed of the lead vehicle, and a relative acceleration of the lead vehicle. The deceleration estimate may be a road attribute deceleration estimate. The road attribute deceleration estimate may be based on a function of a current speed of the host vehicle, a target speed of the host vehicle, and a distance to the target location. The deceleration estimate may be a static object deceleration estimate. The static object deceleration estimate may be based on a current speed of the host vehicle, a target speed of the static object, and a distance to the static object.

[0071] The proactive lane assistance method 6000 includes calculating a feedback force at step 6030. The feedback force may be calculated based on a lead vehicle deceleration estimate, a lead vehicle dynamic deceleration estimate, a road attribute deceleration estimate, a stationary object deceleration estimate, or any combination thereof. The dynamically modified pedal map may be used to adjust the available range of torque requests based on the deceleration estimate.

[0072] The proactive lane assistance method 6000 includes adjusting the driver torque request based on the feedback force calculated in step 6040. Adjusting the driver torque request in step 6040 effectively changes the APO-to-torque conversion to match the driver's expectations. For example, in an open and free-moving situation, the driver may want to relax and take his / her foot off the accelerator. In such a situation, the host vehicle automatically adjusts the APO-to-torque conversion to reduce the maximum deceleration torque request, thereby allowing the host vehicle to coast and cruise as expected. In a traffic jam or where higher speed regulation is required, such as at intersections and parking lots, the driver may expect more deceleration from the host vehicle when the driver takes his / her foot off the accelerator pedal. In such a situation, the host vehicle automatically adjusts the APO-to-torque conversion to increase the maximum deceleration to sufficiently decelerate the vehicle when the driver releases the accelerator pedal. The APO-to-torque conversion may be adjusted based on one or more accelerator maps. The one or more accelerator maps may be associated with a driving mode and may include a normal mode accelerator map, a regenerative mode accelerator map, and a comfort mode accelerator map. The adjustment of the driver torque request may be based on a proactive assistance request. The proactive assistance request may be based on a confidence value of the lead vehicle deceleration estimate. In one example, the proactive assistance request may be a torque request that is subtracted from the nominal torque request for the selected driving mode to adjust the estimate of the driver torque request to better match the driver's expected deceleration in the scene.

[0073] The proactive assistance request may be ramped such that the initial adjustment is incremented at a rate based on the determined scene until a maximum adjustment is reached. The rate of the adjustment to the estimate of the driver torque request may be adjusted based on a curve. For example, the estimate of the driver torque request may be adjusted at a first rate when entering the determined scene, and the estimate of the driver torque request may be adjusted at a second rate when exiting the determined scene. For example, the first rate may be about six times the second rate such that the accelerator feedback force is adjusted quickly to ramp into the adjustment when entering the determined scene, and adjusted at a slower rate to exit the adjustment when exiting the determined scene. Exiting the determined scene in this case may include returning to normal driving conditions when the determined scene is changed, for example, when the lead vehicle exits a highway, or when some other change occurs to the scene.

[0074] As used herein, the term "computer" or "computing device" includes any unit or combination of units, or any part or parts thereof, capable of carrying out any of the methods disclosed herein.

[0075] As used herein, the term "processor" refers to one or more processors, such as one or more special purpose processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more application processors, one or more application specific integrated circuits, one or more application specific standard integrated circuits, one or more field programmable gate arrays, any other type or combination of integrated circuits, one or more state machines, or any combination thereof.

[0076] As used herein, the term "memory" refers to any computer-usable or computer-readable medium or device that can tangibly hold, store, communicate, or carry any signals or information that can be used by or associated with any processor. For example, memory may be one or more read-only memories (ROM), one or more random access memories (RAM), one or more registers, low-power DDR (LPDDR) memories, one or more cache memories, one or more semiconductor memory devices, one or more magnetic media, one or more optical media, one or more magneto-optical media, or any combination thereof.

[0077] As used herein, the term "instructions" may include instructions or expressions for performing any method disclosed herein, or any part or parts thereof, and may be implemented in hardware, software, or any combination thereof. For example, instructions may be implemented as information, such as a computer program stored in a memory, that may be executed by a processor to perform any of the methods, algorithms, aspects, or combinations thereof described herein. In some embodiments, instructions or portions thereof may be implemented as a dedicated processor or circuitry, which may include dedicated hardware for performing any of the methods, algorithms, aspects, or combinations thereof described herein. In some implementations, portions of instructions may be distributed across multiple devices, multiple processors on a single device, that may communicate directly or over a network, such as a local area network, a wide area network, the Internet, or combinations thereof.

[0078] As used herein, the terms "example," "embodiment," "implementation," "aspect," "feature," or "element" indicate serving as an example, example, or illustration. Unless expressly stated, any example, embodiment, implementation, aspect, feature, or element is independent of each other example, embodiment, implementation, aspect, feature, or element and may be used in combination with any other example, embodiment, implementation, aspect, feature, or element.

[0079] As used herein, the terms "determining" and "identifying" or any variation thereof includes selecting, ascertaining, calculating, retrieving, receiving, determining, establishing, obtaining, or otherwise identifying or determining in any manner using one or more of the devices shown and described herein.

[0080] As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X includes A or B" is intended to indicate any natural inclusive permutation. That is, if X includes A, X includes B, or X includes both A and B, then "X includes A or B" is satisfied by any of the above examples. Furthermore, the articles "a" and "an" as used in this application and the appended claims should generally be construed to mean "one or more," unless otherwise specified by the context or a clear reference to the singular form is made.

[0081] Furthermore, although for simplicity of description, the drawings and descriptions herein may include a series of steps or stages or sequences, elements of the methods disclosed herein may occur in various orders or simultaneously. Moreover, elements of the methods disclosed herein may occur with other elements not explicitly shown and disclosed herein. Moreover, not all elements of the methods described herein are required to implement a method according to the present disclosure. Although aspects, features, and elements are described herein in certain combinations, each aspect, feature, or element may be used independently or in various combinations with or without other aspects, features, and elements.

[0082] The above aspects, examples and implementations are described to facilitate understanding of the present disclosure and are not intended to be limiting. To the contrary, the present disclosure encompasses various modifications and equivalent arrangements that are within the scope of the appended claims, which should be interpreted broadest possible to encompass all such modifications and equivalent arrangements as are permitted by law.

Claims

1. 1. A method for use in a host vehicle, comprising: acquiring sensor data for determining a scene associated with an operating environment of the host vehicle, the scene including predicted actions of one or more remote vehicles; calculating a deceleration estimate based on the scene; calculating a feedback force based on the deceleration estimate; adjusting an APO (accelerator pedal output) to torque conversion for one pedal driving based on the calculated feedback force, the APO to torque conversion being adjusted at a ratio based on the scene; A method comprising:

2. The method of claim 1 , wherein the ratio is a first ratio when the scene is detected and a second ratio when the scene change is detected.

3. The method of claim 2 , wherein the first ratio is greater than the second ratio.

4. The method of claim 3 , wherein the first ratio is approximately six times the second ratio.

5. the deceleration estimate is a road deceleration estimate related to road attributes; The method of claim 1 , wherein the road deceleration estimate is based on a function of a current speed of the host vehicle, a target speed of the host vehicle at the location of the road attribute, and a distance to the location of the road attribute.

6. The method of claim 5 , wherein the road attribute is a slope, a descent, a curve, or a stop line.

7. The method of claim 1 , wherein the deceleration estimate is a lead vehicle deceleration estimate based on a function of a current speed of the host vehicle, a target speed of the host vehicle, and a time to reach the target speed.

8. The method of claim 7 , wherein the target speed of the host vehicle is equal to the current speed of the lead vehicle.

9. the deceleration estimate is a static object deceleration estimate associated with a static object; The method of claim 1 , wherein the static object deceleration estimate is based on a function of a current speed of the host vehicle and a distance to the static object.

10. The method of claim 1 , wherein the deceleration estimate is a dynamic lead vehicle deceleration estimate based on a function of a lead vehicle's relative distance, a lead vehicle's relative speed, and a lead vehicle's relative acceleration.

11. a sensor configured to acquire data from an operating environment of the host vehicle; Processor and In a host vehicle comprising: determining a scene based on the acquired data, the scene including predicted actions of one or more remote vehicles; calculating a deceleration estimate based on the scene; calculating a feedback force based on the deceleration estimate; adjusting an APO (accelerator pedal output) to torque conversion for one pedal driving based on the calculated feedback force, the APO to torque conversion being adjusted at a ratio based on the scene; A host vehicle configured to:

12. 12. The host vehicle of claim 11, wherein the ratio is a first ratio when the scene is determined and a second ratio when a change in the scene is determined.

13. The host vehicle of claim 12 , wherein the first ratio is greater than the second ratio.

14. 14. The host vehicle of claim 13, wherein the first ratio is approximately six times the second ratio.

15. the deceleration estimate is a road deceleration estimate related to road attributes; The host vehicle of claim 11 , wherein the road deceleration estimate is based on a function of a current speed of the host vehicle, a target speed of the host vehicle at the location of the road attribute, and a distance to the location of the road attribute.

16. The host vehicle of claim 15 , wherein the road attribute is a slope, a descent, a curve, or a stop line.

17. The host vehicle of claim 11 , wherein the deceleration estimate is a lead vehicle deceleration estimate based on a function of a current speed of the host vehicle, a target speed of the host vehicle, and a time to reach the target speed.

18. 20. The host vehicle of claim 17, wherein the target speed of the host vehicle matches the current speed of the lead vehicle.

19. the deceleration estimate is a static object deceleration estimate associated with a static object; The host vehicle of claim 11 , wherein the static object deceleration estimate is based on a function of a current speed of the host vehicle and a distance to the static object.

20. 12. The host vehicle of claim 11, wherein the deceleration estimate is a dynamic lead vehicle deceleration estimate based on a function of a lead vehicle relative distance, a lead vehicle relative speed, and a lead vehicle relative acceleration.

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