System and method for mitigating vehicle collision with bicycle

Abstract

The disclosure relates to systems and methods for mitigating vehicle collisions with a bicycle. The present disclosure effectively and efficiently mitigates a collision between a vehicle and a bicycle. According to an embodiment, a method for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle is provided. The method comprises the following steps: acquiring surrounding environment information of a first vehicle; acquiring first information associated with the bicycle from the environmental information; receiving, from a second vehicle, second information associated with the bicycle; predicting the track of the bicycle according to the first information and the second information; determining a risk of a collision between the first vehicle and the bicycle based on the predicted trajectory; and providing feedback to a driver of the first vehicle according to the risk of collision and the predicted trajectory.

Description

Technical Field

[0001] Exemplary embodiments of the present disclosure relate to a vehicle safety system, and more particularly, to a collision mitigation system mounted on a vehicle to mitigate a collision with a bicycle. Background Art

[0002] Various approaches have been introduced in the related art for mitigating vehicle collisions. For example, a forward collision warning (FCW) system uses sensors (e.g., radar or cameras) to monitor the road ahead. When an impending collision is detected, the system alerts the driver through visual and / or audible warnings. As another example, automatic emergency braking (AEB) detects a potential forward collision and automatically activates the vehicle's braking system to mitigate the potential collision.

[0003] Despite this, the systems and methods of the related art are primarily designed to mitigate collisions with vehicles with motors (e.g., cars, trucks, etc.). To operate effectively, the sensors onboard the vehicles with motors must have a clear forward field of view. However, the systems and methods of the related art are only minimally effective in mitigating collisions between vehicles with motors and bicycles, at least for the following reasons.

[0004] First, bicycles are typically smaller than vehicles with motors, and their visual recognition is limited when they are driven on the road. Consequently, bicycles can be easily blocked by other objects on the road, and a vehicle's view of a bicycle can be obscured by other vehicles ahead. Related-art safety systems sometimes have limited effectiveness when a clear or direct line of sight to a bicycle is blocked. Consequently, vehicles have difficulty detecting bicycles when other objects are moving around the vehicle or bicycle. This limitation can increase the risk of a collision between a vehicle and a bicycle.

[0005] Furthermore, compared to vehicles, bicycles sometimes have complex movement trajectories. Specifically, bicycle trajectories are inherently dynamic and difficult to predict due to various influencing factors such as weather conditions, road conditions, the load carried by the bicycle, the rider's experience level, and other environmental variables or human-centric factors.

[0006] For example, cyclists' behavior varies greatly based on experience, decision-making, reaction time, personality, and similar factors, which complicate and hinder predicting bicycle movements. For example, a bicycle may suddenly enter the road without prior warning, or a cyclist may suddenly maneuver the bicycle for any specific reason or without any similar reason.

[0007] Furthermore, compared to vehicles with motors, bicycles are lighter and more susceptible to external influences such as gusts of wind, uneven roads, changes in load distribution, and similar influences. Consequently, bicycles are more susceptible to the effects of lean angles than vehicles with motors, potentially causing them to suddenly fall sideways. These factors increase the difficulty and complexity of predicting a bicycle's trajectory based solely on historical or static data.

[0008] Furthermore, cyclists can easily approach and use bicycles without knowledge of traffic rules and regulations. Similarly, drivers from different regions or locations may be unaware of local traffic rules and regulations. For example, in some regions or locations, it is illegal for a powered vehicle to drive alongside a bicycle, requiring powered vehicles to maintain a minimum distance from bicycles, and similar regulations exist. Riders or drivers who are not well-versed in local traffic rules and regulations may fail to notice these and easily violate them, increasing the risk of a collision.

[0009] For at least the reasons stated above, there exists a need to provide a solution that effectively and efficiently mitigates collisions between vehicles and bicycles. Summary of the Invention

[0010] Exemplary embodiments consistent with the present disclosure provide a method, system, and apparatus for effectively and efficiently determining the risk of a collision between a vehicle and a bicycle and mitigating the risk of a subsequent collision.

[0011] According to an embodiment, a method executed by at least one processor of a system in a first vehicle is provided for mitigating a collision between the first vehicle and a bicycle in the vicinity of the first vehicle. The method includes the following steps: obtaining environmental information about the first vehicle's surroundings from at least one onboard sensor; obtaining first information associated with the bicycle from the environmental information, wherein the first information includes at least one of the bicycle's location, the bicycle's type, and the type of rider; receiving second information associated with the bicycle from a second vehicle, wherein the second information includes information captured by at least one onboard sensor of the second vehicle; predicting a trajectory of the bicycle based on the first and second information; determining a risk of collision between the first vehicle and the bicycle based on the predicted trajectory; and providing feedback to the driver of the first vehicle based on the risk of collision and the predicted trajectory.

[0012] According to an embodiment, a system for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle is provided. The system includes: a storage device storing computer-executable instructions; and at least one processor communicatively coupled to the storage device. The at least one processor is configured to execute the instructions to perform the following steps: obtaining environmental information about the surroundings of the first vehicle from at least one onboard sensor; obtaining first information associated with the bicycle from the environmental information, wherein the first information includes at least one of the location of the bicycle, the type of bicycle, and the type of rider riding the bicycle; receiving second information associated with the bicycle from a second vehicle, wherein the second information includes information captured by at least one onboard sensor of the second vehicle; predicting a trajectory of the bicycle based on the first and second information; determining a risk of collision between the first vehicle and the bicycle based on the predicted trajectory; and providing feedback to the driver of the first vehicle based on the risk of collision and the predicted trajectory.

[0013] Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be achieved by practice of the presented embodiments of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A diagram illustrating an exemplary use case for implementing an exemplary embodiment.

[0015] Figure 2 A functional block diagram illustrating an exemplary vehicle system for mitigating a collision between a first vehicle and a bicycle in proximity to the first vehicle, according to one or more embodiments.

[0016] Figure 3A A diagram illustrating an exemplary use case of predicting a bicycle trajectory according to one or more embodiments.

[0017] Figure 3B A side view of a bicycle on a road is shown according to one or more embodiments.

[0018] Figure 4 An exemplary graph correlating and arranging probability distributions of bicycles and strength of steering feedback is shown in accordance with one or more embodiments.

[0019] Figure 5 A block diagram illustrating exemplary components of a vehicle system according to one or more embodiments.

[0020] Figure 6 A flow chart illustrating an exemplary method for mitigating a collision between a first vehicle and a bicycle in proximity to the first vehicle, according to one or more embodiments. DETAILED DESCRIPTION

[0021] Hereinafter, the features, advantages and significance of the exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same elements.

[0022] The following detailed description of exemplary embodiments will refer to the accompanying drawings. The above disclosure provides examples and illustrations and is not intended to be exhaustive or to limit the embodiments to the exact forms disclosed. Modifications and variations can be made in accordance with the above disclosure or can be obtained based on the implementation of the embodiments. Moreover, one or more features or constituent elements of one embodiment can be embedded in another embodiment (or one or more features of another embodiment) or combined with another embodiment (or one or more features of another embodiment). In addition, it will be understood that in the flowcharts and descriptions of the operations provided below, one or more operations can be omitted, one or more operations can be added, one or more operations can be performed (at least partially) simultaneously, and the order of one or more operations can be reversed.

[0023] Even if specific combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible embodiments. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. The dependent claims listed below may sometimes be directly dependent on only one claim, but the disclosure of possible embodiments includes each dependent claim in combination with all other claims in the claim set.

[0024] Unless explicitly stated otherwise, elements, actions, or instructions used in this specification should not be construed as being critical or essential. In addition, when used in this specification, the terms "have," "has," "having," "include," "including," and the like are intended to be open-ended terms. Furthermore, unless explicitly stated otherwise, the phrase "based on" is intended to mean "based at least in part on." Furthermore, "[A] and / or [B]," "at least one of [A] and [B]," or "at least one of [A] or [B]" should be understood to include only A, only B, or both A and B.

[0025] Throughout this specification, references to "one embodiment," "an embodiment," "a non-limiting exemplary embodiment," or similar language mean that a particular feature, structure, or characteristic described in connection with the illustrated embodiment is included in at least one embodiment of the present disclosure. Thus, throughout this specification, the phrases "in one embodiment," "in an embodiment," "in a non-limiting exemplary embodiment," and similar language may sometimes all refer to the same embodiment, but not necessarily.

[0026] Furthermore, the features, advantages, and characteristics described herein may be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize that the present disclosure may be implemented with one or more of the specific features or advantages described in this specification without reference to a particular embodiment. In other examples, additional features and advantages may be recognized in a particular embodiment that are not present in all embodiments of the present disclosure.

[0027] Furthermore, the term "vehicle" as used herein refers to vehicles with a prime mover, such as cars, trucks, buses, motorcycles, and other suitable types of vehicles powered by an engine, motor, or other mechanical component. On the other hand, although the present disclosure is described herein with respect to a "bicycle," it is intended that the exemplary embodiments of the present disclosure may also be applied to other suitable types of vehicles or running components (e.g., skateboards, roller skates, kick scooters, etc.) without departing from the scope of the present disclosure.

[0028] Figure 1This diagram illustrates an exemplary use case for implementing the exemplary embodiment. In this exemplary use case, a bicycle 110 and multiple vehicles 120-150 are traveling on a road 160. Road 160 includes a road edge and lane markings that define two vehicle lanes. Vehicle 120 is traveling parallel to bicycle 110, while vehicles 130-150 are traveling behind vehicle 120 and bicycle 110.

[0029] In the related art, vehicle 140's view of bicycle 110 is blocked by vehicle 130, so vehicle 140 may not be able to detect bicycle 110. Similarly, vehicle 150's view of bicycle 110 is blocked by vehicles 120 and 130, so vehicle 150 may not be able to detect bicycle 110. These situations pose a risk of collision between one or more of vehicles 140 and 150 and bicycle 110.

[0030] On the other hand, the rider of bicycle 110 and the driver of vehicle 120 may have violated one or more local traffic rules and regulations, but the rider and / or driver may not have noticed these violations. For example, the following situations may exist: the distance between vehicle 120 and bicycle 110 is small, not meeting the minimum passing distance specified in laws related to safe overtaking or laws related to vulnerable persons in traffic; road 160 may be located in a no-passing zone where overtaking of bicycle 110 is prohibited; road 160 is located in an area that includes restrictions on passing bicycles, so passing bicycle 110 violates rules and regulations; road 160 is reserved for vehicles with prime movers, so bicycle 110 should not enter road 160.

[0031] The exemplary embodiments of the present disclosure enable the vehicle to efficiently determine the risk of collision and perform one or more actions to mitigate the risk of collision even when the vehicle cannot directly see the bicycle. Furthermore, one or more feedback is provided to the driver and rider, thereby enabling the driver and rider to obtain information related to the real-time status. For example, the driver and rider are provided with an alert for a potential collision and can then take appropriate actions to avoid the collision. In the event that the driver and / or rider violates one or more traffic rules and regulations (or predicts that the driver and / or rider will violate one or more traffic rules and regulations), the vehicle promptly notifies the associated driver and / or rider.

[0032] According to an embodiment, a system ("vehicle system" in this specification) is provided to and installed on a vehicle. The system is configured to: obtain first information about a bicycle through one or more on-board sensors, and at the same time, receive second information captured by one or more surrounding vehicles. By effectively utilizing the combined information to achieve collaborative sensing between vehicles on the road, the vehicle system can effectively detect the presence of a bicycle, thereby predicting the trajectory of the bicycle and determining the risk of collision between the vehicle and the bicycle. In order to prevent a collision, subsequent operations including the following actions are performed: providing feedback (e.g., visual alarms, auditory alarms, steering control, etc.) to the driver of the vehicle; sharing information with surrounding drivers and riders; notifying the vehicle driver and / or rider of local traffic laws and traffic rules; and similar actions.

[0033] Figure 2 A functional block diagram of an exemplary vehicle system 200 according to one or more embodiments is shown. Vehicle system 200 is installed in one or more vehicles (e.g., vehicles 120-150) and can interact with other vehicle systems and components to facilitate efficient and effective collision risk determination and mitigation.

[0034] As an example, the vehicle system 200 interacts with the vehicle's braking system in a manner that controls the vehicle's speed according to a collision risk determined by the vehicle system 200, interacts with the vehicle's steering system in a manner that provides a steering wheel vibration alert and / or active steering correction according to the determined collision risk and / or the predicted trajectory of the bicycle, interacts with an in-vehicle infotainment (IVI) system in a manner that provides visual alerts and / or auditory alerts to the driver, and interacts with other appropriate systems in the vehicle in a manner that actively assists the driver of the vehicle in collision avoidance maneuvers and improves overall vehicle control and stability in potential collision situations.

[0035] like Figure 2As shown, the vehicle system 200 includes a plurality of functional modules 210 to 280. One or more of the modules 210 to 280 are implemented in various forms of hardware, firmware, or a combination of hardware and software. In this regard, it is intended that one or more operations described in this specification in relation to each of the modules 210 to 280 are performed by hardware (e.g., a processor, etc.) when executing software or computer-executable instructions for implementing the modules 210 to 280. Moreover, it is intended that, without departing from the scope of the present disclosure, one or more of the modules 210 to 280 are integrated into a single module (e.g., the receiver module 230 and the transmitter module 260 are coupled as a transceiver module, the object detector module 220, the trajectory predictor module 240, and the collision avoidance module 250 are coupled as a collision mitigation module, etc.).

[0036] The sensing module 210 is Figure 5 Various onboard sensors (described further herein) collect data or information ("sensed data") and then, before providing the sensed data to other modules of vehicle system 200, perform one or more operations to process the sensed data, integrate meaningful data, and improve data accuracy. For example, sensing module 210 may perform one or more data filtering operations to reduce noise in the sensed data, perform one or more data correction operations to correct systematic errors in the sensed data, perform one or more data fusion operations to integrate or compile the sensed data from multiple sensors to comprehensively and consistently represent the vehicle's surrounding conditions, and perform similar operations. After processing the sensed data, module 210 provides the processed data to other modules of vehicle system 200 (e.g., object detector module 220, trajectory predictor module 240, etc.) for further utilization or processing.

[0037] The object detector module 220 is configured to detect and identify one or more objects of interest around the vehicle. Specifically, the object detector module 220 is configured to detect and extract various information associated with the bicycle (hereinafter referred to as "bicycle information") from the sensing data provided by the sensing module 210, such as the location of the bicycle, the type of bicycle, the type of rider, the load on the bicycle, the speed of the bicycle, the rider's steering behavior, signs indicating whether the rider has violated one or more traffic rules and regulations (or whether the rider is about to violate one or more traffic rules and regulations), and similar information. In some embodiments, the object detector module 220 is configured to detect and extract various information associated with the road on which the bicycle is traveling from the sensing data provided by the sensing module 210, such as bicycle lane information (e.g., bicycle lanes separated from motor vehicles by physical barriers, buffered bicycle lanes with painted buffer areas between bicycle lanes and road lanes, bicycle lanes marked with painted lines or dedicated colors, bicycle lane widths, etc.), infrastructure along the road (e.g., barriers, guardrails, etc.), and similar information.

[0038] Depending on the embodiment, the sensory data includes image data captured by one or more vehicle-mounted cameras, and the object detector module 220 performs one or more image processing operations to detect and extract information about objects of interest (e.g., bicycles, cyclists, bicycle lanes, etc.) from the one or more images. For example, the object detector module 220 performs one or more operations such as edge detection, histogram analysis, shape or texture detection, and similar operations to identify the objects of interest from the image data. In some embodiments, the cameras may include a forward-facing camera capable of detecting bicycles in front of the vehicle, a side-facing camera capable of detecting bicycles next to the vehicle, and a rear-facing camera capable of detecting bicycles behind the vehicle.

[0039] According to an embodiment, the object detector module 220 is configured to classify or categorize detected objects into various classes based on various characteristics such as size, shape, and similar features. For example, when a bicycle is detected, the object detector module 220 classifies the bicycle into specific types of bicycles such as road bicycles, mountain bikes, single-speed / fixed gear bicycles, cargo bikes, throttle-assist electric bicycles, pedal-assist electric bicycles, and similar types. Similarly, the object detector module 220 classifies riders based on their age, behavior, facial expression, and similar features. Furthermore, the object detector module 220 may also classify bicycles based on the amount of load on the front / rear of the bicycle, the number of child seats mounted on the bicycle, and similar loads.

[0040] Without departing from the scope of the present disclosure, the object detector module 220 may also be configured to detect and classify other objects such as vehicles, pedestrians, road markings, traffic signs, road surface conditions, lane configurations of roads, bicycle lanes, and other appropriate objects, or settings around the vehicle or along the route of travel. This information is used by the vehicle system 200 to determine whether the driver and / or rider has violated one or more traffic rules and regulations (or whether the driver and / or rider is about to violate one or more traffic rules and regulations). When information about an object of interest is obtained, the object detector module 220 provides the information to other modules of the vehicle system 200 (e.g., trajectory predictor module 240, collision avoidance module 250, etc.) for further use or processing.

[0041] Receiver module 230 is configured to receive data or information from one or more devices outside of vehicle system 200. According to embodiments, receiver module 230 performs vehicle-to-vehicle (V2V) communication with one or more surrounding vehicles and vehicle-to-infrastructure (V2I) communication with external devices such as roadside infrastructure or radio broadcast stations, thereby receiving data or information from other vehicles and external devices in real time or substantially real time. The received information includes bicycle information and environmental information captured by one or more onboard sensors of surrounding vehicles, traffic information, information about local traffic rules and regulations, information about surrounding vehicles (e.g., driving speed, current location, etc.), and similar information. According to embodiments, the received information also includes warnings of potential collisions between surrounding vehicles and bicycles, warnings regarding potential emergency braking of surrounding vehicles to avoid collisions, red signs indicating that a particular cyclist and / or driver of a surrounding vehicle may not be familiar with local traffic rules and regulations, and similar warnings or alerts.

[0042] According to an embodiment, the receiver module 230 is configured to process the received information or data. For example, the receiver module 230 decodes the encoded information, performs error checking, performs data integrity checks, and similar actions. Furthermore, the receiver module 230 provides the received information to other modules of the vehicle system 200 (e.g., the trajectory predictor module 240, the collision avoidance module 250, etc.) for further use or processing.

[0043] Trajectory predictor module 240 is configured to predict the trajectory of the bicycle in real time (or substantially real time) based on information obtained from object detector module 220 and receiver module 230. As described above with respect to modules 220 and 230, the information provided to trajectory predictor module 240 includes various bicycle information (e.g., bicycle speed, rider's steering behavior, bicycle location, bicycle type, rider type, bicycle load, etc.) captured by one or more onboard sensors of the vehicle and one or more onboard sensors of surrounding vehicles, and various environmental information (e.g., weather conditions, time of day, lighting conditions, road conditions, traffic conditions, local traffic rules and regulations, etc.). In some embodiments, the information provided to trajectory predictor module 240 includes information related to the road or lane on which the bicycle is traveling.

[0044] The trajectory predictor module 240 predicts or anticipates the future trajectory of the bicycle based on the received information. For example, the module 240 predicts one or more of the bicycle's future motion, the path the bicycle may take, the bicycle's potential position relative to the vehicle and / or surrounding vehicles, the bicycle's relative or absolute speed, and similar information based on the bicycle information.

[0045] According to an embodiment, the trajectory predictor module 240 adjusts or fine-tunes the predicted bicycle trajectory based on environmental information. For example, the module 240 considers real-time conditions such as road conditions, weather conditions, surrounding visibility, and other conditions that may affect the bicycle trajectory, and then adjusts the predicted bicycle trajectory based on the real-time conditions.

[0046] Referring to the diagram showing an exemplary use case of predicting a bicycle trajectory according to one or more embodiments, Figure 3A .exist Figure 3A In the example shown in FIG. 1 , trajectory predictor module 240 determines, based on environmental information, that the road or lane ahead of the bicycle has a curve. Module 240 then determines the likely trajectory of the bicycle, taking into account, for example, the bicycle's speed, the load on the bicycle, and / or the bicycle's direction of travel. Module 240 then adjusts the determined trajectory based on, for example, road conditions, weather conditions, and similar conditions.

[0047] According to an embodiment, the trajectory predictor module 240 is further configured to determine or predict the lean angle of the bicycle. For example, the trajectory predictor module 240 determines one or more potential lean angles of the bicycle based on bicycle information (e.g., bicycle type, bicycle load, bicycle speed, rider behavior, etc.).

[0048] Referring to a side view of a bicycle on a road Figure 3B .like Figure 3B As shown, the bicycle's lean angle defines the angle at which the bicycle tilts or falls sideways (i.e., toward the road) relative to a vertical axis. In other words, the lean angle represents the angular change in the bicycle from an upright position. According to an embodiment, module 240 determines a stability threshold based on bicycle information and environmental information, wherein the threshold is a value at which the bicycle's lean angle, if exceeded, is likely to cause the bicycle to lose stability and fall sideways. The stability threshold is affected by various real-time factors, including the bicycle's speed, the coefficient of friction between the tires and the road surface, and similar factors.

[0049] In some embodiments, module 240 is configured to compute at least one probability distribution for the bicycle, wherein the probability distribution takes into account the likelihood or uncertainty of the bicycle and provides a probabilistic representation of one or more future actions of the bicycle. According to embodiments, module 240 predicts or computes a probability distribution for the predicted bicycle trajectory and predicted lean angle as a time series. Alternatively or in addition, module 240 may also be configured to compute a probability distribution associated with the lateral position of the bicycle. The probability distribution defines or represents the likelihood or probability of one or more events, such as the probability of the bicycle tipping over or falling sideways due to excessive lean, the probability of the bicycle oversteering (turning too sharply) or understeering (not turning sufficiently) during a turn on the predicted trajectory, the probability of the bicycle colliding with an obstacle or hazard along the predicted trajectory, the probability of the bicycle violating local traffic rules and regulations (e.g., illegally trespassing in a prohibited area, driving parallel to a vehicle, etc.) along the predicted trajectory, and similar probabilities.

[0050] According to an embodiment, module 240 uses one or more probabilistic filtering algorithms (e.g., Kalman filtering) to predict the above-mentioned probability distribution. Furthermore, module 240 uses bicycle information (e.g., rider information, bicycle type, load information, etc.) to calculate or compute the above-mentioned probability distribution. Furthermore, module 240 adjusts or fine-tunes the probability distribution, for example, based on one or more pieces of information in the bicycle information. For example, module 240 adjusts or fine-tunes the probability distribution based on the type of rider (e.g., increasing the probability of the bicycle suddenly falling sideways based on a determination that the rider falls into the category of "child" or "elderly"), the type and / or number of loads mounted on the bicycle (e.g., widening the probability distribution of trajectory and tilt angle based on a determination that multiple child seats are mounted on the bicycle), the type of bicycle drive (e.g., narrowing the probability distribution of trajectory and tilt angle based on a determination that the bicycle is an electric-assisted bicycle), and similar information.

[0051] According to an embodiment, module 240 is configured to continuously (or periodically) update trajectory predictions, lean angle predictions, and the probability distribution of predicted trajectories and lean angles based on real-time (or substantially real-time) information obtained from onboard sensors and surrounding vehicles, taking into account the bicycle's most recent state and environmental conditions. Furthermore, module 240 provides information about the predicted bicycle trajectory, the predicted lean angle of the bicycle, and / or the probability distribution of the predicted trajectory and lean angle to other modules of vehicle system 200 (e.g., collision avoidance module 250, transmitter module 260, etc.) for further use or processing.

[0052] Further references Figure 2 Collision avoidance module 250 is configured to determine the risk of collision between the vehicle and the bicycle based at least on the predicted trajectory of the bicycle. According to embodiments, module 250 compares one or more of the relative positions of the vehicle and bicycle, the speeds of the vehicle and bicycle, and the predicted trajectories of the vehicle and bicycle to calculate or compute the risk of collision. According to embodiments, module 250 dynamically assesses the risk of collision by continuously (or periodically) receiving updated bicycle trajectories from module 240.

[0053] Furthermore, module 250 determines the collision risk by considering whether the rider and / or driver has violated one or more traffic rules and regulations (or whether the rider and / or driver has a probability of violating one or more traffic rules and regulations). According to an embodiment, module 250 increases the determined collision risk by a specified percentage based on a determination that the rider / driver has violated one or more traffic rules and regulations or a determination that the rider / driver is likely not proficient in traffic rules and regulations. For example, module 250 may increase the determined collision risk by 5% based on a determination that the rider has likely violated one traffic rule, increase the determined collision risk by 10% based on a determination that the rider has violated one traffic rule, and perform similar actions.

[0054] When a risk of collision is determined, module 250 performs (or instructs other modules to perform) one or more operations for reducing the risk of collision and mitigating a potential collision between the vehicle and the bicycle.

[0055] According to an embodiment, module 250 instructs user interface (UI) module 270 to generate one or more user interfaces that provide one or more feedback to the driver of the vehicle based on the risk of collision and / or the predicted trajectory. For example, based on the risk of collision, UI module 270 generates at least one graphical user interface (GUI) including a visual alert corresponding to the risk of collision (e.g., the visual alert has a first color intensity corresponding to a first level of risk of collision and a second color intensity corresponding to a second level of risk of collision, includes a first icon corresponding to the first level of risk of collision and a second icon corresponding to the second level of risk of collision, etc.) and / or at least one voice user interface (VUI) including an auditory alert corresponding to the risk of collision (e.g., the auditory alert has a first volume corresponding to the first level of risk of collision and a second volume corresponding to the second level of risk of collision, etc.). Thereafter, module 270 provides at least one GUI to a visual device within the vehicle (e.g., a head-up display (HUD), an infotainment display, a display of a navigation device, etc.), and provides at least one VUI to an auditory device within the vehicle (e.g., a speaker, a buzzer, etc.), thereby presenting visual and / or auditory alerts to the driver.

[0056] According to an embodiment, module 250 instructs vehicle control module 280 to perform one or more vehicle control operations based on the risk of collision and / or the predicted trajectory. For example, vehicle control module 280 calculates the optimal steering angle and trajectory adjustment required to maintain vehicle stability and a safe separation distance from the bicycle. Based on the optimal steering angle, vehicle control module 280 then provides steering guidance for adjusting the vehicle's steering wheel to create a virtual barrier or safety zone between the vehicle and the bicycle. As another example, vehicle control module 280 sends a signal to the vehicle's electronic control unit (ECU) to activate a vibration motor located within the steering wheel, causing the vibration motor to generate steering wheel vibrations based on the risk of collision (e.g., the steering wheel vibrations have a first strength / intensity corresponding to a first level of collision risk and a second strength / intensity corresponding to a second level of collision risk, etc.). It is contemplated that vehicle control module 280 may also perform other appropriate operations, such as controlling the vehicle's braking system or throttle system, to reduce the risk of collision and avoid a collision, without departing from the scope of this disclosure.

[0057] According to an embodiment, module 250 provides information related to collision risk, such as information associated with collision risk avoidance, information related to visual and / or auditory alerts (obtained from UI module 270), and / or information related to vehicle control (obtained from vehicle control module 280), to transmitter module 260. Transmitter module 260 is configured to receive the collision risk avoidance information from module 250 and information about the predicted trajectory and lean angle of the bicycle, as well as their probability distribution, from module 240. Transmitter module 260 then transmits the above information to one or more surrounding vehicles via V2V communication and / or to a device associated with the bicycle rider via a cellular network.

[0058] Depending on the implementation where module 250 determines that the driver and / or cyclist has violated one or more traffic rules and regulations (or the driver and / or cyclist has a certain probability of violating one or more traffic rules and regulations), module 250 instructs module 260 to share information with one or more associated users. In some embodiments, module 260 provides an alert or notification along with the associated traffic rules or regulations to the driver and / or cyclist associated with the violation. Alternatively, module 260 may also broadcast the driver and / or cyclist's information along with the associated traffic rules or regulations to surrounding vehicles or bicycles.

[0059] According to an embodiment, one or more modules in modules 210 to 280 utilize and effectively utilize one or more artificial intelligence (AI) and / or machine learning (ML) models to perform one or more operations described in this specification. For example, module 220 utilizes one or more AI / ML models to detect and identify one or more objects from sensory data, module 240 utilizes one or more AI / ML models to predict or calculate the probability distribution of the predicted bicycle trajectory and lean angle, and module 250 utilizes one or more AI / ML models to determine appropriate operations for reducing the risk of collision and perform similar actions. In some embodiments, module 220 utilizes one or more AI / ML models (e.g., semantic segmentation models, etc.) to detect the lane in which the bicycle is traveling.

[0060] Furthermore, one or more of modules 210 to 280 are configured to continuously (or periodically) perform one or more operations described herein based on the real-time (or substantially real-time) status of the bicycle, the vehicle, and the surrounding conditions. For example, module 210 continuously acquires sensing data from one or more onboard sensors, module 220 continuously detects objects of interest, module 230 continuously acquires information from one or more surrounding vehicles and / or external devices, module 240 continuously predicts the trajectory and lean angle of the bicycle, module 250 continuously predicts collision risk and determines appropriate actions associated with the collision risk, module 270 continuously updates the GUI and / or VUI to present the latest information, module 280 continuously adjusts vehicle control operations according to the latest collision risk, and module 260 continuously provides the latest information to one or more surrounding vehicles and riders and performs similar actions.

[0061] Next, refer to Figure 1 , a non-limiting exemplary use case (implementing the operation of the vehicle system 200) will be described. For the purpose of simple description, it is assumed that each of the vehicles 120 to 150 has the vehicle system 200 installed therein.

[0062] Refer again Figure 1 The vehicle system in vehicle 130 obtains environmental information about the vehicle 130's surroundings from at least one onboard sensor. The system then determines first information associated with bicycle 110 based on the environmental information. Furthermore, the system in vehicle 130 obtains second information associated with bicycle 110 from vehicle 120. The first information includes bicycle information about bicycle 110, such as one or more of bicycle 110's location, bicycle 110's type, and the type of rider riding bicycle 110, as well as similar information. The second information includes bicycle information determined by the vehicle 120 system based on environmental information about the vehicle 120's surroundings (captured by one or more onboard sensors in vehicle 120), as well as information about the trajectory and lean angle of bicycle 110 predicted by the vehicle 120 system, information about the probability distribution of the predicted trajectory and lean angle, information about collision risk determined by the vehicle 120 system, information about an alarm or warning related to a potential collision, and information about vehicle control performed by the vehicle 120 system.

[0063] After obtaining the first and second information, the system in vehicle 130 predicts the trajectory of bicycle 110 based on the first and second information. In some embodiments, it is contemplated that the system in vehicle 130 also obtains additional information associated with bicycle 110 from other vehicles (e.g., the vehicle in front of vehicle 120, a vehicle in the oncoming lane, etc.), and then predicts the trajectory of bicycle 110 based on the additional information. Furthermore, the system in vehicle 130 predicts the lean angle of bicycle 110 and calculates a probability distribution for the predicted bicycle trajectory and lean angle.

[0064] When at least the trajectory of the bicycle is predicted, the system in the vehicle 130 determines the risk of collision between the vehicle 130 and the bicycle 110 based on the predicted trajectory.

[0065] Therefore, the system within vehicle 130 provides feedback to the driver of vehicle 130 based on the risk of collision and the predicted trajectory. For example, based on the risk of collision, vehicle 130's system generates steering feedback (e.g., steering wheel vibration and / or steering guidance that modifies steering maneuvers to steer vehicle 130 away from bicycle 110, creating a virtual barrier between them). Furthermore, based on the risk of collision, vehicle 130's system generates at least one GUI that includes a visual alert (e.g., a collision warning icon with a color corresponding to the risk of collision, a flashing signal with an intensity corresponding to the risk of collision, an animation simulating a collision, etc.), and then presents the at least one GUI to the driver via a display within vehicle 130 (e.g., a head-up display, an infotainment display, a navigation device display, etc.). Furthermore, based on the risk of collision, vehicle 130's system generates an auditory alert (e.g., an alarm sound or warning buzzer with a volume corresponding to the risk of collision, a voice prompt describing the level of collision risk, etc.).

[0066] According to an embodiment, the system of vehicle 130 compares the latest risk of collision with the previous risk of collision and then adjusts the intensity of the feedback based on the comparison. Specifically, based on a determination that the latest / current risk of collision is higher than the previous risk of collision, the system increases the intensity of the feedback (e.g., increasing the color intensity of a visual alert displayed on a display of the first vehicle, increasing the volume of an audible alert output through a speaker of the first vehicle, increasing the intensity of vibration of the steering wheel of the first vehicle, etc.). Conversely, based on a determination that the latest / current risk of collision is lower than the previous risk of collision, the system decreases the intensity of the feedback (e.g., decreasing the color intensity of the visual alert, decreasing the volume of the audible alert, decreasing the intensity of vibration of the steering wheel, etc.).

[0067] According to an embodiment, the system of the vehicle 130 is configured to adjust the intensity of the feedback according to the probability distribution of the bicycle. Figure 4 .like Figure 4 As shown, a bicycle has a probability distribution associated with the lateral position of the bicycle. The probability distribution is predicted or calculated in real time (or approximately in real time) by the system of vehicle 130 based on first information (obtained from the sensing module and / or object detector module) and second information (obtained from one or more external devices or external vehicles via the receiver module). In this regard, the system of vehicle 130 can also calculate or predict an intensity distribution of steering feedback based on the probability distribution of the bicycle, wherein the intensity distribution represents the predicted or expected intensity of steering feedback at each lateral position of the bicycle. Therefore, the system of vehicle 130 adjusts the real-time intensity of the steering feedback based on the intensity distribution. It is intended that the system can also adjust the intensity of other feedback (e.g., visual alarms, auditory alarms, steering guidance, vehicle braking, etc.) according to the same scheme without departing from the scope of this disclosure.

[0068] Refer again Figure 1 Vehicle 130's system communicates with surrounding vehicles (e.g., vehicles 120, 140, and 150) and exchanges information with them. For example, environmental information captured by vehicle 130's onboard sensors may include images of bicycle 110 and vehicle 120. This information can be useful for vehicle 120 in determining or strengthening collision risk assessment and mitigation measures. Therefore, upon acquiring environmental information, vehicle 130's system transmits this information to vehicle 120.

[0069] Similarly, because vehicles 140 and 150 do not have direct visual recognition of bicycle 110, information acquired or determined by vehicle 130's system, such as bicycle information, predicted bicycle trajectory and lean angle, bicycle probability distribution, collision risk, and similar information, is useful to vehicles 140 and 150. Therefore, vehicle 130's system communicates with vehicles 140 and 150, sharing information with them. This allows vehicles 140 and 150 to effectively determine the risk of collision with bicycle 110 and promptly execute one or more actions to mitigate the collision risk, even when vehicles 140 and 150 do not have direct or clear visual recognition of bicycle 110.

[0070] In addition, vehicles 120-150 continuously (or periodically) communicate with each other to exchange information. In this case, vehicles 120-150 use information acquired from other vehicles to cross-reference or verify information acquired and / or determined by onboard sensors, and use information acquired from one vehicle to cross-reference or verify information acquired from another vehicle, and perform similar actions. For example, vehicle 130 uses information acquired from vehicle 120 to verify the accuracy or integrity of information acquired by its onboard sensors. As another example, vehicle 140 uses information acquired from vehicle 120 to verify information acquired from vehicle 130, or vice versa, before using this information to determine collision risk or perform any action to mitigate collision risk. This improves the accuracy of the information used to determine collision risk, thereby enabling accurate determination of collision risk and actions to mitigate collision risk.

[0071] Furthermore, when a collision risk is determined, one or more of vehicles 120-150 notifies or warns the rider of bicycle 110. For example, vehicle 120 provides a warning of a potential collision between vehicle 120 (or any of vehicles 130-150) and bicycle 110 to a device associated with the rider. The warning is provided via FM (Frequency Modulation) wireless communication, AM (Amplitude Modulation) wireless communication, a cellular network, satellite wireless communication, cellular network communication (e.g., 3G (3rd Generation), 4G (4th Generation), 5G (5th Generation)), Bluetooth (registered trademark), Wi-Fi (Wireless Fidelity) (registered trademark), and / or other appropriate wireless communication. In this way, a warning can be provided to the rider in a timely manner regarding a potential collision, allowing the rider to take preemptive action (e.g., leaving the road, changing the bicycle's trajectory, reducing speed, etc.) to avoid the collision. Similarly, when a violation (or potential violation) of at least one traffic rule or traffic regulation is determined, one or more of vehicles 120 to 150 notifies or warns the associated driver and / or rider, allowing the associated driver and / or rider to notice the violation and take timely action to avoid the violation.

[0072] Next, a description is provided of exemplary components of a vehicle system that can be installed in one or more vehicles to mitigate collisions with bicycles. Figure 5 . Figure 5 System 500 and Figure 2 Therefore, it is intended that, unless otherwise specified, features described in this specification with respect to system 200 and system 500 are mutually applicable. Furthermore, one or more components of system 200 (e.g., modules 210 to 280) are implemented by one or more components of system 500.

[0073] like Figure 5 As shown, the system 500 includes at least one bus 510, at least one processor 520, at least one memory 530, at least one storage component 540, at least one vehicle-mounted sensor 550, at least one input / output component 560, and at least one communication interface 570. It is contemplated that the system 500 may also include other components without departing from the scope of the present disclosure. Figure 5 For example, in some embodiments, the input / output component 560 may include a dedicated input component and a dedicated output component that operate independently of each other, and the system 500 may include multiple storage components 540, and similar variations are possible.

[0074] At least one bus 510 includes one or more components that enable communication between components of system 500 and between such components and other systems / components within the vehicle. For example, bus 510 includes a Controller Area Network (CAN) bus, a Local Interconnect Network (LIN) bus, an Ethernet bus, or other types of buses that enable real-time (or substantially real-time) communication between vehicle components such as components 520-570, actuators, electronic control units (ECUs), and similar vehicle components.

[0075] At least one processor 520 is implemented by hardware, firmware, or a combination of hardware and software. According to an embodiment, the processor 520 includes a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC) and / or another type of processing component or computing component. In some embodiments, the processor 520 includes one or more processors that can be programmed to perform one or more operations described in this specification. In addition, the processor 520 may also include multiple processing units, each of which is specially designed to perform a specific operation (for example, to Figure 2 Each of the modules 210 to 280 is assigned a dedicated processing unit, etc.).

[0076] At least one memory 530 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic storage device or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) that stores information and / or instructions for use by the processor 520. At least one storage component 540 stores information and / or software related to the operation and use of the system 500. For example, the storage component 540 includes a corresponding drive and includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state drive), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium.

[0077] According to an embodiment, the storage component 540 is configured to store computer-readable instructions or computer-executable instructions for implementing one or more modules of the system (e.g., modules 210 to 280), one or more AI / ML models for implementing one or more operations of modules 210 to 280, one or more sensory data, one or more bicycle information, one or more environmental information, one or more data obtained from external devices or other devices, one or more predicted bicycle trajectories, one or more predicted bicycle lean angles, one or more probability distributions of bicycles, one or more risks of collision between a vehicle and a bicycle, one or more past operations performed to mitigate previous collision risks, one or more feedback provided to the driver, one or more information shared with another vehicle, one or more map data, one or more traffic rules and regulations corresponding to one or more locations in the map data, and / or similar information. The storage component 540 provides the stored information to the memory 530 for execution by the processor 520.

[0078] The at least one vehicle-mounted sensor 550 includes one or more sensors mounted on the vehicle and configured to detect, measure, and capture various sensing data. For example, the at least one sensor 550 includes: an accelerometer that measures and captures data associated with the acceleration / deceleration, vehicle speed, and / or vehicle travel distance of the vehicle; an image sensor (e.g., a camera, etc.) that detects and captures image data of the surroundings or near the vehicle; a light detection and ranging (LiDAR) sensor that detects and captures data associated with light of one or more spectrums such as the visible spectrum, infrared spectrum, ultraviolet spectrum, and / or other spectrums; a voice sensor (e.g., a microphone, etc.) that detects and captures voice data inside and / or outside the vehicle; a temperature sensor that measures and captures data associated with the temperature inside and / or outside the vehicle; a position sensor (e.g., a Global Positioning System (GPS) receiver, an inertial measurement unit (IMU), etc.) Unit), etc.), which measures and captures data associated with the location, position and / or orientation of the vehicle; contact sensors (e.g., pressure sensors, impact detectors, etc.), which detect and capture data between a part of the vehicle and an object; air sensors, which measure and capture data associated with the air inside and / or outside the vehicle (e.g., oxygen level, pollution level, humidity level, etc.); meteorological sensors (e.g., rain sensors, snow sensors, wind speed sensors, visual recognition sensors, etc.), which measure and capture weather conditions around the vehicle (e.g., the possibility and intensity of rainfall, the presence or absence of snow or snow accumulation, wind direction, the possibility of gusts, the possibility of fog / mist / haze, etc.); proximity sensors (e.g., ultrasonic sensors, LiDAR, radar, etc.), which measure the distance between the vehicle and surrounding objects (e.g., bicycles); FM / AM receivers, which detect wireless signals from wireless stations on road infrastructure to receive information (e.g., weather forecasts to be broadcast, traffic updates, emergency alerts, local traffic rules and regulations, etc.); and other appropriate sensors equipped on the vehicle.

[0079] At least one input / output component 560 includes one or more input components (e.g., a touch screen display, a keyboard, a keypad, a mouse, buttons, switches, and / or a microphone) that enable the system 500 to receive information via user input, for example. Additionally or alternatively, the input / output component 560 includes one or more output components (e.g., a display, a speaker, a navigation device, one or more light-emitting diodes (LEDs), etc.) that provide output information from the system 500.

[0080] At least one communication interface 570 includes a component such as a transceiver (e.g., a transceiver and / or a separate receiver and transmitter) that enables the system 500 to communicate with other devices, for example, via a wired connection, a wireless connection, or a combination of a wired and wireless connection. The communication interface 570 enables the system 500 to receive information from one or more devices outside the vehicle (e.g., one or more devices in other vehicles, devices installed on road infrastructure, devices on a bicycle, etc.) and / or provide information to one or more devices outside the vehicle. For example, the communication interface 570 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, an RF (Radio Frequency) interface, a Universal Serial Bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like.

[0081] According to one or more embodiments, the communication interface 570 includes at least one input / output (I / O) interface, at least one network interface, at least one sensor interface, at least one storage interface, or similar interfaces that enable components 520 to 560 to communicate with other devices outside the vehicle. In addition, the communication interface 570 may also include one or more application programming interfaces (APIs) that enable the system 500 (or one or more components included in the system 500) to communicate with one or more software applications (e.g., software applications deployed on the rider's device, software applications installed on vehicle systems of surrounding vehicles, etc.). According to an embodiment, the receiver module 230 and the transmitter module 260 (or one or more operations associated therewith) are implemented via the communication interface 570.

[0082] System 500 is responsive to at least one processor 520 executing a program for implementing Figure 2 The computer-executable instructions of one or more functional modules among functional modules 210 to 280 in the embodiment of the present invention perform one or more operations described in this specification. These computer-executable instructions are stored in a non-transitory computer-readable storage medium such as memory 530 and / or storage component 540. In this specification, a computer-readable storage medium is defined as a non-transitory storage device. A storage device includes storage space within a single physical storage device or storage space extended across multiple physical storage devices.

[0083] Computer-executable instructions (e.g., software instructions, etc.) may also be read into the memory 530 and / or storage component 540 from another computer-readable storage medium or another device (e.g., a remote server, external storage, etc.) via the communication interface 570. When executed, the computer-executable instructions stored in the memory 530 and / or storage component 540 cause the processor 520 to perform one or more processes described in this specification. In addition to or in lieu of, hard-wired circuitry may be used in place of, or in combination with, software instructions to perform one or more processes described in this specification. Therefore, the implementations described in this specification are not limited to any particular combination of hardware circuitry and software.

[0084] Figure 6 A flow chart illustrating an exemplary method 600 for mitigating a collision between a first vehicle and a bicycle in proximity to the first vehicle, according to one or more embodiments. The method 600 is performed by at least one processor (e.g., processor 520) of a system (e.g., vehicle system 200, vehicle system 500, etc.) installed in the first vehicle when executing associated computer instructions stored in at least one storage device (e.g., memory 530, storage device 540, etc.) of the system.

[0085] Reference Figure 6 In operation 610, at least one processor is configured to obtain environmental information about the surroundings of the first vehicle from at least one onboard sensor (e.g., onboard sensor 550). The environmental information includes information about one or more objects around the vehicle (e.g., a bicycle, a road obstacle, another vehicle, etc.), lane information (the lane in which the bicycle is traveling), weather conditions (e.g., sunny, rainy, foggy, snowy, etc.), timing information (e.g., current time, daytime, nighttime, etc.), lighting conditions (e.g., bright environment, dim environment, etc.), road conditions (e.g., slippery roads, uneven roads, winding roads, etc.), traffic information (e.g., congestion, local traffic rules and regulations, etc.), a history of previous bicycle accidents near the point where the bicycle is traveling, and similar information. According to an embodiment, the at least one onboard sensor includes a camera, and the environmental information includes at least one image captured by the camera.

[0086] When executing the computer readable instructions for implementing the sensing module, operation S610 is performed by at least one processor, and its specific operations are described above. Figure 2 Therefore, for the sake of brevity, further description of the specific operations for obtaining environmental information is omitted below.

[0087] When the environmental information is acquired, method 600 proceeds to operation S620, in which at least one processor is configured to acquire first information associated with a bicycle from the environmental information. The first information includes one or more of the location of the bicycle, the type of bicycle, and the type of rider riding the bicycle. In embodiments where the environmental information includes at least one image, at least one processor performs one or more image processing operations on the at least one image to identify an object of interest (e.g., a bicycle, a rider, etc.), and then performs one or more classification operations / categorization operations to determine the type of bicycle, the type of rider, and the location of the bicycle.

[0088] When executing the computer-readable instructions for implementing the object detector module, operation S620 is performed by at least one processor, and its specific operations are described above with reference to FIG. Figure 2 Therefore, for the sake of brevity, further description related to the specific operations for detecting and extracting the first information from the environmental information is omitted below.

[0089] Further references Figure 6 In operation S630, at least one processor is configured to receive second information associated with the bicycle. The second information is obtained from the second vehicle and includes information captured by one or more onboard sensors of the second vehicle and information calculated based on the information. For example, the second information includes environmental information captured by the onboard sensors of the second vehicle, bicycle information obtained by the second vehicle's system based on the associated environmental information, bicycle trajectory information predicted by the second vehicle's system, bicycle lean angle information predicted by the second vehicle's system, bicycle probability distribution predicted or calculated by the second vehicle's system, collision risk determined by the second vehicle's system, feedback generated by the second vehicle's system based on the associated collision risk and predicted trajectory, potential collision alerts / warnings generated by the second vehicle's system, red signs indicating that the rider and / or driver of the second vehicle may not be familiar with local traffic rules and regulations, and similar information. Additionally or alternatively, the at least one processor may also be configured to obtain the second information directly from a device equipped with the bicycle (e.g., the rider's mobile device).

[0090] When executing the computer-readable instructions for implementing the receiver module, operation S630 is performed by at least one processor, and its specific operations are described above with reference to FIG. Figure 2 Therefore, for the sake of brevity, further description of the specific operation for receiving the second information is omitted below.

[0091] It should be noted that operations S610 to S630 may be performed in any appropriate order without departing from the scope of the present disclosure. For example, operation S630 may be performed simultaneously with operation S610 or operation S620, performed after operation S610 or operation S620, or performed before operation S610 or operation S620.

[0092] After obtaining the first information and the second information, method 600 proceeds to operation S640, in which at least one processor is configured to predict a trajectory of the bicycle based on the first information and the second information. According to an embodiment, the at least one processor further predicts a lean angle of the bicycle and calculates a probability distribution of the bicycle based on the predicted trajectory and the predicted lean angle.

[0093] When executing the computer-readable instructions for implementing the trajectory predictor module, operation S640 is performed by at least one processor, and its specific operations are described above with reference to FIG. Figure 2 Therefore, for the sake of brevity, further description of the specific operations for predicting the bicycle trajectory is omitted below.

[0094] When a bicycle trajectory is predicted, method 600 proceeds to operation S650, in which at least one processor is configured to determine the risk of collision between the first vehicle and the bicycle based on the predicted trajectory. When executing computer-readable instructions for implementing the collision avoidance module, operation S650 is performed by at least one processor, and its specific operations are described above with reference to FIG. Figure 2 Therefore, for the sake of brevity, further description of the specific operations for determining the collision risk is omitted below.

[0095] When a collision risk is determined, method 600 proceeds to operation S660, where at least one processor is configured to provide feedback to the driver of the first vehicle based on the collision risk and the predicted trajectory. According to an embodiment, providing feedback includes generating steering feedback based on the collision risk. The steering feedback includes at least one of steering wheel vibration and steering guidance away from the bicycle.

[0096] According to an embodiment, the feedback includes a warning of a potential collision between the first vehicle and the bicycle. In this regard, providing the feedback includes: generating at least one GUI including a visual warning based on the risk of collision; and presenting the at least one GUI to the driver via a display within the first vehicle. Additionally or alternatively, providing the feedback includes: generating an audible warning based on the risk of collision; and presenting the audible warning to the driver via a speaker within the first vehicle.

[0097] According to an embodiment, in addition to providing feedback to the driver of the first vehicle, the at least one processor also provides feedback (e.g., a potential collision warning, etc.) to a third vehicle and / or provides feedback (e.g., a potential collision warning, etc.) to a device associated with the bicycle rider. In some embodiments, the at least one processor provides feedback to the third vehicle using the first information (obtained in operation S620) or without using the first information.

[0098] When executing computer readable instructions for implementing one or more modules of the UI module, the vehicle control module, and the transmitter module, operation S660 is performed by at least one processor, and its specific operations are described above. Figure 2 Therefore, for the sake of brevity, further description of the specific operations for providing feedback is omitted below.

[0099] When the feedback is provided, method 600 terminates or ends. Alternatively, method 600 may return to operation S610, causing the at least one processor to repeatedly perform operations S610 to S660 for at least a specified period. In this way, the vehicle system continuously or repeatedly determines the risk of collision with the bicycle based on the real-time status of the bicycle and actual environmental conditions.

[0100] Figure 6 The operations shown are simple exemplary operations performed by the vehicle system of the exemplary embodiment, and it is intended that the scope of the present disclosure should not be limited thereto. Specifically, at least one processor of the system may also be configured to implement or execute one or more additional operations of modules 210 to 280.

[0101] For example, when providing feedback to the driver and / or rider, the at least one processor may also compare the latest / current risk of a collision (determined in operation S650) with the earlier risk of a collision (determined in an earlier operation cycle), and then adjust the intensity of the feedback based on the comparison. For example, the at least one processor may increase the intensity of the feedback (e.g., by increasing the color intensity of a visual alert, increasing the volume of an audible alert, increasing the intensity of steering wheel vibration, etc.) based on a determination that the latest / current risk of a collision is higher than the earlier risk of a collision. Conversely, the system may decrease the intensity of the feedback (e.g., by decreasing the color intensity of a visual alert, decreasing the volume of an audible alert, decreasing the intensity of steering wheel vibration, etc.) based on a determination that the latest / current risk of a collision is lower than the earlier risk of a collision. In this approach, the feedback presented to the driver and / or rider can adapt to changing conditions in real time (or substantially real time), thereby improving the effectiveness of collision prevention and mitigation.

[0102] With at least the foregoing in mind, exemplary embodiments of the present disclosure address issues arising from the dynamic nature of bicycle behavior and states. Specifically, exemplary embodiments enable a vehicle to capture environmental information and share this captured information with surrounding vehicles, thereby enabling collaborative sensing between vehicles in real time (or substantially real time). Thus, exemplary embodiments enable vehicles to efficiently and effectively predict and prevent collisions with bicycles, particularly in scenarios where clear or direct visual identification of the bicycle is unavailable.

[0103] Furthermore, a driver or rider who is not well versed in local traffic rules and regulations is promptly notified and provided with an alert, and the driver or rider performs corrective actions, which also avoids violating local traffic rules and regulations and reduces the risk of collision.

[0104] Furthermore, the exemplary embodiment effectively utilizes adaptive predictive analytics based on real-time bicycle information and environmental conditions, thereby enabling the vehicle system to dynamically adjust collision risk prediction and collision risk mitigation operations according to changing conditions.

[0105] To this end, exemplary embodiments provide robust and accurate collision risk prediction in dynamic environments, increasing the effectiveness of collision avoidance actions and improving safety for both drivers and cyclists in situations where bicycle trajectory and behavior are difficult to predict. Furthermore, exemplary embodiments provide human-centric safety enhancements by automatically presenting appropriate feedback to connected users, resulting in more beneficial predictions and improved collision avoidance when the road includes a diverse range of users (e.g., drivers of various vehicle types, cyclists of varying backgrounds, etc.).

[0106] The features, advantages, and significance of the exemplary embodiments described above in this specification are merely a portion of the present disclosure and are not intended to be exhaustive or to limit the scope of the present disclosure. The following provides a further description of the features, constituent elements, configuration, operation, and implementation of the exemplary embodiments of the present disclosure, as well as the associated technical advantages and significance.

[0107] It is to be understood that the specific order or hierarchy of functional blocks within the processes / flowcharts disclosed in this specification is an example of an illustrative method. It is to be understood that the specific order or hierarchy of functional blocks within the processes / flowcharts may also be reconfigured based on design preferences. Moreover, some functional blocks may also be combined or omitted. The appended method claims present the elements of the various functional blocks in a sample order and are not intended to be limited to the specific order or hierarchy presented.

[0108] Some embodiments relate to integrated systems, methods, and / or computer-readable media at any possible level of technical detail. Furthermore, as described above in this specification, one or more of the aforementioned components described above may also be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may also include at least one processor). The computer-readable medium includes a non-transitory computer-readable storage medium having computer-readable program instructions for causing the processor to perform operations.

[0109] A computer-readable storage medium is a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium is, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof, but is not limited thereto. A non-exhaustive list of more specific examples of computer-readable storage media includes the following media, i.e., a mechanically encoded device including a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM (Erasable Programmable Read Only Memory) or flash memory), a static random access memory (SRAM: Static Random Access Memory), a portable compact disc read-only memory (CD-ROM: Compact Disc Read Only Memory), a digital versatile disc (DVD), a memory stick (memory stick), a floppy disk, a punch card, or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. When used in this specification, computer-readable storage media should not be interpreted as temporary signals themselves such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses passing through optical cables), or electrical signals transmitted through wires.

[0110] The computer-readable program instructions described in this specification can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network includes copper transmission cables, optical transmission cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transmits the computer-readable program instructions to the computer-readable storage medium in each computing / processing device for storage.

[0111] The computer-readable program code / instructions for performing the operations may be any one of assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages, wherein the one or more programming languages ​​include object-oriented programming languages ​​such as Smalltalk, C++, or similar languages, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions are executed entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer is connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection is made to an external computer (for example, via the Internet using an Internet service provider). In some embodiments, in order to execute a scheme or operation, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), uses state information of computer-readable program instructions to personalize the electronic circuit, thereby executing the computer-readable program instructions.

[0112] These computer-readable program instructions are provided to a general-purpose computer, a special-purpose computer, or other programmable data processing device to manufacture a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create components for implementing the functions / behaviors specified in the functional block or multiple functional blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct the computer, programmable data processing device, and / or other devices to function according to a specific scheme, so that the computer-readable storage medium having the stored instructions has an article of manufacture, wherein the article of manufacture includes instructions for implementing the scheme of functions / behaviors specified in the functional block or multiple functional blocks in the flowchart and / or block diagram.

[0113] Computer-readable program instructions can also be loaded into a computer, other programmable data processing device, or another apparatus, and a series of operating steps are performed on the computer, other programmable device, or other apparatus to generate a computer installation process so that the instructions executed on the computer, other programmable device, or other apparatus implement the functional / behavioral schemes determined in the functional blocks or multiple functional blocks in the flowchart and / or block diagram.

[0114] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer-readable media of various embodiments. In this regard, each functional block in a flowchart or block diagram represents a module, segment, or portion of instructions having one or more executable instructions for implementing a specified logical function. The method, computer system, and computer-readable medium may also include additional functional blocks, fewer functional blocks, different functional blocks, or functional blocks configured differently than those depicted in the figures. In alternative embodiments, the functions described in the functional blocks may also occur in a sequence that deviates from the sequence depicted in the figures. For example, two functional blocks shown in succession may actually be executed simultaneously or approximately simultaneously, and the functional blocks may sometimes be executed in reverse order depending on the related functions. It is also noted that each functional block in the block diagram and / or flowchart, as well as combinations of functional blocks in the block diagram and / or flowchart, may be implemented by a special-purpose hardware-based system that performs the specified functions or behaviors, or a combination of special-purpose hardware and computer instructions.

[0115] It will be apparent that the systems and / or methods described in this specification may also be implemented using various forms of hardware, firmware, or a combination of hardware and software. The actual specific control hardware or software code used to implement these systems and / or methods does not limit the implementation. Therefore, the operation and behavior of the systems and / or methods are described in this specification without reference to specific software code. It is understood that software and hardware can be designed to implement the systems and / or methods based on the description in this specification.

Claims

1. A method executed by at least one processor of a system in a first vehicle to mitigate a collision of the first vehicle with a bicycle in the vicinity of the first vehicle, comprising the steps of: Acquiring environmental information surrounding the first vehicle from at least one onboard sensor; acquiring first information associated with the bicycle from the environmental information, wherein the first information includes at least one of a location of the bicycle, a type of the bicycle, and a type of a rider riding the bicycle; receiving second information associated with the bicycle from a second vehicle, wherein the second information includes information captured by one or more onboard sensors of the second vehicle; predicting a trajectory of the bicycle according to the first information and the second information; determining a risk of collision between the first vehicle and the bicycle based on the predicted trajectory; and Feedback is provided to a driver of the first vehicle based on the risk of collision and the predicted trajectory.

2. The method according to claim 1, wherein The feedback includes an alert of a potential collision between the first vehicle and the bicycle.

3. The method according to claim 2, further comprising the steps of: One or more of the first information and the warning of the potential collision is provided to a third vehicle.

4. The method according to claim 2 or 3, further comprising the steps of: An alert of the potential collision is provided to a device associated with the rider of the bicycle.

5. The method according to any one of claims 1 to 4, wherein The at least one vehicle-mounted sensor includes a camera, and the environmental information includes at least one image captured by the camera.

6. The method according to any one of claims 1 to 5, wherein Providing the feedback includes generating steering feedback based on the risk of collision, wherein the steering feedback includes at least one of vibration of a steering wheel and steering guidance away from the bicycle.

7. The method according to any one of claims 1 to 5, wherein The steps of providing said feedback include: generating a graphical user interface (GUI) including a visual warning based on the risk of collision; and The GUI is presented to the driver via a display within the first vehicle.

8. The method according to any one of claims 1 to 5, wherein The steps of providing said feedback include: generating an audible alert based on the risk of collision; and The audible alert is presented to the driver via a speaker in the first vehicle.

9. The method according to any one of claims 1 to 8, further comprising the steps of: comparing the risk of the collision with an earlier risk of collision; increasing the intensity of the feedback based on a determination that the risk of collision is higher than an earlier risk of collision; as well as Based on a determination that the risk of collision is lower than an earlier risk of collision, the intensity of the feedback is reduced.

10. The method according to claim 9, wherein: The step of increasing the intensity of the feedback includes one or more of the following actions: increasing the color intensity of a visual warning displayed on a display of the first vehicle; increasing the volume of an auditory warning output through a speaker of the first vehicle; and increasing the strength of vibration of a steering wheel of the first vehicle. The step of reducing the intensity of the feedback includes one or more of the following actions: reducing the color intensity of the visual alarm; reducing the volume of the auditory alarm; and reducing the strength of the vibration of the steering wheel.

11. A system for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle, comprising: a storage memory storing computer-executable instructions; and at least one processor communicatively coupled to the storage memory, in, The at least one processor is configured to execute the instructions to perform the following steps: Acquiring environmental information surrounding the first vehicle from at least one onboard sensor; acquiring first information associated with the bicycle from the environmental information, wherein the first information includes at least one of a location of the bicycle, a type of the bicycle, and a type of a rider riding the bicycle; receiving second information associated with the bicycle from a second vehicle, wherein the second information includes information captured by one or more onboard sensors of the second vehicle; predicting a trajectory of the bicycle according to the first information and the second information; determining a risk of collision between the first vehicle and the bicycle based on the predicted trajectory; and Feedback is provided to a driver of the first vehicle based on the risk of collision and the predicted trajectory.

12. The system according to claim 11, wherein The feedback includes an alert of a potential collision between the first vehicle and the bicycle.

13. The system according to claim 12, wherein: The at least one processor is further configured to provide one or more of the first information and the warning of the potential collision to a third vehicle.

14. The system according to claim 12 or 13, wherein: The at least one processor is further configured to provide an alert of the potential collision to a device associated with the rider of the bicycle.

15. The system according to any one of claims 11 to 14, wherein: The at least one vehicle-mounted sensor includes a camera, and the environmental information includes at least one image captured by the camera.

16. The system according to any one of claims 11 to 15, wherein: The at least one processor is configured to generate steering feedback based on the risk of collision, wherein the steering feedback includes at least one of vibration of a steering wheel and steering direction away from the bicycle, thereby providing the feedback.

17. The system according to any one of claims 11 to 15, wherein: The at least one processor is configured to: generating a graphical user interface (GUI) including a visual warning based on the risk of collision; as well as presenting the GUI to the driver via a display in the first vehicle, The feedback is thereby provided.

18. The system according to any one of claims 11 to 15, wherein: The at least one processor is configured to: generating an audible alert based on the risk of collision; as well as presenting the audible warning to the driver via a speaker in the first vehicle, The feedback is thereby provided.

19. The system according to any one of claims 11 to 18, wherein: The at least one processor is further configured to: comparing the risk of the collision with an earlier risk of collision; increasing the intensity of the feedback based on a determination that the risk of collision is higher than an earlier risk of collision; as well as Based on a determination that the risk of collision is lower than an earlier risk of collision, the intensity of the feedback is reduced.

20. The system of claim 19, wherein: The at least one processor is configured to increase the intensity of the feedback by one or more of the following actions: increasing the color intensity of a visual alert displayed on a display of the first vehicle; increasing the volume of an auditory alert output through a speaker of the first vehicle; and increasing the intensity of vibration of a steering wheel of the first vehicle. The at least one processor is configured to reduce the intensity of the feedback by one or more of the following actions: reducing the color intensity of the visual alert; reducing the volume of the auditory alert; and reducing the strength of the vibration of the steering wheel.

Images