System and method for mitigating collisions between bicycles and vehicles

Abstract

To reduce effectively and efficiently a risk of a collision between a vehicle and a bicycle.SOLUTION: According to an embodiment, a method is provided for mitigating a collision between a first vehicle and a bicycle located in the vicinity of the first vehicle. The method includes: acquiring environmental information around the first vehicle; acquiring first information associated with the bicycle from the environmental data; receiving second information associated with the bicycle from a second vehicle; predicting a trajectory of the bicycle based on the first and 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 based on the risk of collision and the predicted trajectory.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] Exemplary embodiments of the present disclosure relate to vehicle safety systems, particularly collision mitigation systems implemented in a vehicle to mitigate collisions with bicycles.

Background Art

[0002] In related art, various approaches have been introduced to mitigate vehicle collisions. For example, a Forward Collision Warning (FCW) system uses sensors (such as radar or camera) to monitor the road ahead and alerts the driver with visual and / or audible warnings when an imminent collision is detected. As another example, Automatic Emergency Braking (AEB) detects potential forward collisions and automatically activates the vehicle's braking system to mitigate potential collisions.

[0003] Nevertheless, the systems and approaches in related art are mainly designed to mitigate collisions between motor vehicles (such as cars, trucks, etc.), and for effective operation, the on-board sensors of motor vehicles are required to have a clear forward view. However, the systems and approaches of related art are less effective in mitigating collisions between motor vehicles and bicycles for at least the following reasons.

[0004] First, bicycles usually have a smaller size than motor vehicles and have limited visibility when being operated on the road. Therefore, bicycles may be easily blocked by other objects on the road, and the visibility of the vehicle to the bicycle may be easily blocked by other vehicles ahead. The safety systems of related art may have their effects limited when a clear or direct line of sight to the bicycle is blocked. For this reason, it becomes difficult for the vehicle to detect the bicycle when there are other objects moving around the vehicle or the bicycle. This limitation may increase the risk of collisions between the vehicle and the bicycle.

[0005] Furthermore, bicycles can have more complex movement trajectories compared to vehicles. Specifically, bicycle trajectories are inherently dynamic and unpredictable due to various influencing factors such as weather conditions, road surface conditions, the load carried by the bicycle, the cyclist's experience level, and other environmental factors, or human-centered factors.

[0006] For example, cyclists' behavior varies based on experience, decision-making, reaction time, personality, and similar factors, which makes predicting bicycle movements complex and difficult. For instance, bicycles may enter the road without prior signaling, and cyclists may suddenly maneuver their bikes without any specific reason or similar.

[0007] Furthermore, compared to motorized vehicles, bicycles are lighter and more susceptible to external influences such as gusts of wind, uneven road surfaces, changes in weight distribution, and similar factors. Therefore, compared to motorized vehicles, bicycles are more susceptible to the effects of tilt angles and can suddenly tip over. These factors increase the difficulty and complexity of predicting bicycle trajectories based solely on historical or static data.

[0008] In addition, cyclists can easily access and use bicycles without knowledge of traffic rules and regulations. Similarly, vehicle drivers from different regions or locations may not be aware of local traffic rules and regulations. For example, in some regions or locations, it is illegal for motorized vehicles to travel alongside bicycles, motorized vehicles should maintain a minimum distance from bicycles, and similar rules exist. Cyclists or drivers who are not familiar with local traffic rules and regulations may easily violate them without realizing it, simultaneously increasing the risk of collisions.

[0009] At least for the reasons stated above, there is a need to provide solutions that effectively and efficiently mitigate collisions between vehicles and bicycles. [Overview of the project]

[0010] Exemplary embodiments consistent with this disclosure provide methods, systems, and devices for effectively and efficiently determining the risk of collision between a vehicle and a bicycle and for mitigating the subsequent risk of collision.

[0011] According to the embodiment, a method is provided which is performed by at least one processor of the system in the first vehicle in order to mitigate a collision between the first vehicle and a bicycle in the vicinity of the first vehicle. The method includes the steps of: acquiring environmental information around the first vehicle from at least one onboard sensor; acquiring first information associated with the bicycle from the environmental information, the first information including one or more of the bicycle's position, the bicycle's type, and the type of cyclist riding the bicycle; receiving second information associated with the bicycle from the second vehicle, the second information including information captured by one or more onboard sensors of the second vehicle; predicting the bicycle's trajectory according to the first and second information; determining the risk of a collision between the first vehicle and the bicycle based on the predicted trajectory; and providing feedback to the driver of the first vehicle according to the risk of a collision and the predicted trajectory.

[0012] According to an embodiment, a system is provided for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle. The system includes memory storage for storing computer executable instructions and at least one processor communicatively coupled to the memory storage. The at least one processor is configured to execute instructions to perform the steps of: obtaining environmental information around the first vehicle from at least one onboard sensor; obtaining first information associated with a bicycle from the environmental information, the first information including one or more of the bicycle's position, the bicycle's type, and the type of cyclist riding the bicycle; receiving second information associated with a bicycle from a second vehicle, the second information including information captured by one or more onboard sensors of the second vehicle; predicting the bicycle's trajectory according to the first and second information; determining the risk of a collision between the first vehicle and the bicycle based on the predicted trajectory; and providing feedback to the driver of the first vehicle according to the risk of collision and the predicted trajectory.

[0013] Additional embodiments may be partially described in the following description, partially revealed therein, or realized by the practice of the embodiments presented in this disclosure. [Brief explanation of the drawing]

[0014] [Figure 1] Figure 1 shows an exemplary use case in which an exemplary embodiment is implemented. [Figure 2] Figure 2 shows a functional block diagram of an exemplary vehicle system for mitigating collisions between a first vehicle and a bicycle in the vicinity of the first vehicle, according to one or more embodiments. [Figure 3A] Figure 3A shows an exemplary use case in which the trajectory of a bicycle is predicted according to one or more embodiments. [Figure 3B] Figure 3B shows a side view of a bicycle on a road according to one or more embodiments. [Figure 4]Figure 4 shows an exemplary graph, aligned in relation to the probability distribution of a bicycle and the intensity of steering feedback, according to one or more embodiments. [Figure 5] Figure 5 shows a block diagram of exemplary components of a vehicle system according to one or more embodiments. [Figure 6] Figure 6 shows a flowchart illustrating an exemplary method for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle, according to one or more embodiments. [Modes for carrying out the invention]

[0015] The features, advantages, and significance of exemplary embodiments of this disclosure are described below with reference to the accompanying drawings. In the accompanying drawings, similar reference numerals indicate similar elements.

[0016] A detailed description of exemplary embodiments follows with reference to the accompanying drawings. The foregoing disclosure provides examples and descriptions, but is not intended to be exhaustive or to limit embodiments to the exact forms disclosed. Modifications and variations are possible in light of the foregoing disclosure or can be obtained from the implementation of the embodiments. Furthermore, one or more features or components of one embodiment may be incorporated into 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 should be understood that in the flowcharts and descriptions of operations provided below, one or more operations may be omitted, one or more operations may be added, one or more operations may be performed (at least partially) simultaneously, and the order of one or more operations may be changed.

[0017] Even if specific combinations of features are enumerated in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible embodiments. In fact, many of these features can be combined in embodiments not specifically enumerated in the claims and / or disclosed in the specification. Each of the dependent claims described below may directly depend on only one claim, but the disclosure of possible embodiments includes each dependent claim in combination with all other claims in the set of claims.

[0018] Any element, action, or instruction used herein should not be construed as essential or necessary unless expressly stated otherwise. Furthermore, when used herein, terms such as “having,” “having,” “including,” and “containing” are intended to be open-ended. In addition, the phrase “based on” is intended to mean “based at least in part” unless expressly stated otherwise. Furthermore, “[A] and / or [B],” “at least one of [A] and [B],” or “at least one of [A] or [B]” should be understood as including A only, B only, or both A and B.

[0019] Throughout this specification, references to “one embodiment,” “embodiment,” “non-limiting exemplary embodiment,” or similar language mean that certain features, structures, or characteristics described in relation to the embodiments shown are included in at least one embodiment of this disclosure. Therefore, throughout this specification, the phrases “one embodiment,” “in an embodiment,” “non-limiting exemplary embodiment,” and similar language may, but not necessarily, refer to the same embodiment.

[0020] Furthermore, the described features, advantages, and characteristics of the present disclosure can be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize that, in light of the description herein, the present disclosure can be implemented without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages that may not be present in all embodiments of the present disclosure may be recognized in certain embodiments.

[0021] Furthermore, the term "vehicle" as described herein refers to a motor vehicle such as a car, truck, bus, motorcycle, and other suitable types of automobiles powered by an engine, motor, or other mechanical means. On the other hand, although the present disclosure is described herein with respect to "bicycles", it is contemplated that the exemplary embodiments of the present disclosure are also applicable to other suitable types of non-motorized vehicles or means of locomotion (e.g., skateboards, roller skates, kick scooters, etc.) without departing from the scope of the present disclosure.

[0022] FIG. 1 shows a diagram of an exemplary use case in which an exemplary embodiment is implemented. In this exemplary use case, a bicycle 110 and a plurality of vehicles 120 - 150 are moving on a road 160. The road 160 comprises a road edge and lane lines defining two vehicle lanes. Vehicle 120 is traveling parallel to bicycle 110, while vehicles 130 - 150 are traveling behind vehicle 120 and bicycle 110.

[0023] In the related art, since the line of sight to bicycle 110 is blocked by vehicle 130, vehicle 140 may not be able to detect bicycle 110. Similarly, since the line of sight to bicycle 110 is blocked by vehicle 120 and vehicle 130, vehicle 150 may not be able to detect bicycle 11。This poses a risk of collision between one or more of vehicles 140 - 150 and bicycle 110.

[0024] On the one hand, the cyclist of the bicycle 110 and the driver of the vehicle 120 may have violated one or more local traffic rules and regulations, but the cyclist and / or the driver may not be aware of them. For example, the distance between the vehicle 120 and the bicycle 110 may be narrow and may not meet the minimum overtaking distance defined in the law regarding safe overtaking or the law regarding vulnerable road users, and the road 160 may be located in a no-overtaking area where overtaking the bicycle 110 is prohibited. The area where the road 160 is located may include restrictions on parallel driving with bicycles. Therefore, parallel driving with the bicycle 110 violates the rules and regulations. Since the road 160 is for motor vehicles only, there are situations where the bicycle 110 should not enter the road 160, etc.

[0025] Exemplary embodiments of the present disclosure enable a vehicle to efficiently determine a collision risk and perform one or more operations to reduce the collision risk even when a bicycle is not directly visible. Further, one or more feedbacks are provided to the driver and the cyclist, thereby enabling the driver and the cyclist to obtain information regarding the real-time situation. For example, the driver and the cyclist can be alerted about a potential collision and then take appropriate actions to avoid the collision. If the driver and / or the cyclist is violating (or is predicted to violate) one or more traffic rules and regulations, the vehicle timely notifies the relevant driver and / or the cyclist.

[0026] According to the embodiment, a system (the “Vehicle System” as it is provided herein) is provided and implemented in a vehicle. The System is configured to acquire first information about a bicycle through one or more onboard sensors and, at the same time, to receive second information captured by one or more surrounding vehicles. By utilizing the combined information, cooperative sensing between vehicles on the road is achieved, and the Vehicle System can effectively detect the presence of a bicycle, thereby predicting the bicycle's trajectory and determining the risk of collision between the vehicle and the bicycle. Subsequent actions, including providing feedback to the vehicle driver (e.g., visual alerts, auditory alerts, steering control, etc.), sharing information with surrounding drivers and cyclists, notifying the vehicle driver and / or cyclist about local traffic rules and regulations, and similar actions, are taken to prevent a collision.

[0027] Figure 2 shows a functional block diagram of an exemplary vehicle system 200 according to one or more embodiments. The vehicle system 200 is implemented in one or more vehicles (e.g., vehicles 120-150) and can interact with other systems and components of the vehicle, thereby facilitating efficient and effective determination and mitigation of collision risk.

[0028] For example, the vehicle system 200 interacts with the vehicle's braking system to control the vehicle's speed in accordance with the collision risk determined by the vehicle system 200; interacts with the vehicle's steering system to provide steering vibration alerts and / or active steering corrections in accordance with the determined collision risk and / or the predicted trajectory of the bicycle; interacts with the in-vehicle infotainment (IVI) system to provide visual and / or auditory alerts to the driver; and interacts with other appropriate systems in the vehicle to actively assist the vehicle's driver in collision avoidance maneuvers and to enhance overall vehicle control and stability in potential collision situations.

[0029] As shown in Figure 2, the vehicle system 200 includes a plurality of functional modules 210-280. One or more of modules 210-280 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 herein for each of modules 210-280 are performed by hardware (e.g., a processor) when executing software or computer executable instructions for implementing modules 210-280. Furthermore, without departing from the scope of this disclosure, it is intended that one or more of modules 210-280 are integrated into a single module (for example, the receiver module 230 and the transmitter module 260 are coupled to a transceiver module, and the object detector module 220, the trajectory predictor module 240, and the collision avoidance module 250 are coupled to a collision mitigation module, etc.).

[0030] The sensing module 210 collects data or information ("sensing data" as herein) from various onboard sensors (further described below with respect to Figure 5), and then performs one or more operations to process the sensing data to integrate meaningful data and improve data accuracy before providing the sensing data to other modules of the vehicle system 200. For example, the sensing module may perform one or more data filtering operations to reduce noise in the sensing data, one or more data calibration operations to correct systematic errors in the sensing data, and one or more data fusion operations to integrate or compile sensing data from multiple sensors to provide a comprehensive and consistent representation of the surrounding conditions of the vehicle, and similar operations. After processing the sensing data, the module 210 provides the processed data to other modules of the vehicle system 200 (e.g., object detector module 220, trajectory predictor module 240, etc.) for further use or processing.

[0031] 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 from sensing data provided by the sensing module 210 various information associated with the bicycle ("bicycle information" as herein), such as the bicycle's position, type of bicycle, type of cyclist, load on the bicycle, bicycle speed, cyclist's steering behavior, flags indicating whether the cyclist is violating (or attempting to violate) one or more traffic rules and regulations, and the like. In some embodiments, the object detector module 220 is configured to detect and extract from sensing data provided by the sensing module 210 various information associated with the road on which the bicycle is traveling, such as bicycle lane information (e.g., bicycle lanes separated from motorized vehicles by physical barriers, buffer bicycle lanes with painted buffer zones between bicycle lanes and road lanes, bicycle lanes marked with painted lines or dedicated colors, bicycle lane width, etc.), roadside infrastructure (e.g., barriers, guardrails, etc.), and the like.

[0032] According to the embodiment, the sensing data includes image data captured by one or more onboard cameras, and the object detector module performs one or more image processing operations to detect and extract information about an object of interest (e.g., a bicycle, a cyclist, a bicycle lane, etc.) from one or more images. For example, the object detector module 220 identifies an object of interest from the image data by performing one or more operations such as edge detection, histogram analysis, shape or texture detection, and the like. 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 to the side of the vehicle, and a rear-facing camera capable of detecting bicycles behind the vehicle.

[0033] According to the embodiment, the object detector module 220 is configured to classify or categorize detected objects into various classes based on their respective characteristics, such as size, shape, and similar characteristics. For example, upon detecting a bicycle, the object detector module 220 classifies the bicycle into a specific type of bicycle, such as a road bike, mountain bike, single-speed / fixed-gear bicycle, cargo bicycle, throttle-assist electric bicycle, pedal-assist electric bicycle, and similar characteristics. Similarly, the object detector module 220 classifies cyclists according to their age, behavior, facial expression, and similar characteristics. Furthermore, the object detector module 220 may classify bicycles according to the amount of cargo on the front / rear of the bicycle, the number of child seats mounted on the bicycle, and similar characteristics.

[0034] Without departing from the scope of this disclosure, the object detector module 220 may be configured to detect and classify vehicles, pedestrians, road markings, traffic signs, road surface conditions, road lane configurations, bicycle lanes and other appropriate objects, or settings around the vehicle or along the route of travel. Such information is used by the vehicle system 200 to determine whether the driver and / or cyclist is in violation of (or is about to violate) one or more traffic rules and regulations. Once information on an object of interest is obtained, the object detector module 220 provides the information to other modules of the vehicle system 200 (e.g., a trajectory predictor module 240, a collision avoidance module 250, etc.) for further use or processing.

[0035] The receiver module 230 is configured to receive data or information from one or more devices outside the vehicle system 200. According to the embodiment, the 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 broadcasting stations, thereby receiving data or information from other vehicles and external devices in real time or near real time. The received information includes bicycle information and environmental information captured by one or more onboard sensors of the surrounding vehicles, traffic information, information on local traffic rules and regulations, information on surrounding vehicles (e.g., speed, current location, etc.), and similar information. According to the embodiment, the received information further includes alerts or warnings such as alerts for potential collisions between surrounding vehicles and bicycles, alerts for potential sudden braking by surrounding vehicles to avoid collisions, red flags indicating that a particular cyclist and / or a particular driver of a surrounding vehicle may not be familiar with local traffic rules and regulations, and similar information.

[0036] According to the 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 checking, and similar operations. 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.

[0037] The trajectory predictor module 240 is configured to predict the bicycle's trajectory in real time (or near real time) based on information acquired from the object detector module 220 and the receiver module 230. As described above with respect to modules 220 and 230, the information provided to the trajectory predictor module 240 includes various bicycle information (e.g., bicycle speed, cyclist steering behavior, bicycle position, bicycle type, cyclist type, load on the bicycle, etc.) 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 the trajectory predictor module 240 includes information about the road or lane the bicycle is traveling on.

[0038] Based on the received information, the trajectory predictor module 240 predicts or forecasts the future trajectory of the bicycle. For example, based on the bicycle information, module 240 predicts the future movement of the bicycle, possible paths the bicycle may take, the potential position of the bicycle relative to vehicles and / or surrounding vehicles, the relative or absolute speed of the bicycle, and one or more of the same kind.

[0039] According to the embodiment, the trajectory predictor module 240 adjusts or fine-tunes the predicted bicycle trajectory based on environmental information. For example, module 240 considers real-time conditions such as road surface 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.

[0040] Refer to Figure 3A, which shows an exemplary use case in which the trajectory of a bicycle is predicted according to one or more embodiments. In the example of Figure 3A, the trajectory predictor module 240 determines, based on environmental information, that a curve appears in the road or lane ahead of the bicycle. Therefore, module 240 determines the possible trajectory that the bicycle may take, taking into account, for example, the bicycle's speed, the load on the bicycle, and / or the direction of travel. Therefore, module 240 adjusts the determined trajectory based on, for example, road surface conditions, weather conditions, and similar factors.

[0041] According to the embodiment, the trajectory predictor module 240 is further configured to determine or predict the bicycle's tilt angle. For example, the trajectory predictor module 240 determines one or more potential tilt angles of the bicycle based on bicycle information (e.g., bicycle type, bicycle load, bicycle speed, cyclist behavior, etc.).

[0042] Refer to Figure 3B, which shows a side view of a bicycle on a road. As shown in Figure 3B, the bicycle's tilt angle defines the angle at which the bicycle is tilted or inclined laterally (i.e., toward the road) relative to its vertical axis. In other words, the tilt angle represents the change in angle from the upright position of the bicycle. According to the embodiment, module 240 determines, based on bicycle information and environmental information, a stability threshold at which the bicycle may lose stability and fall laterally when the bicycle's tilt angle is exceeded. The stability threshold is influenced by various real-time factors, including the bicycle's speed, the coefficient of friction between the tires and the road surface, and similar factors.

[0043] In some embodiments, module 240 is configured to compute at least one probability distribution of a bicycle, which provides a probabilistic representation of one or more future actions of the bicycle, taking into account the possibilities or uncertainties of the bicycle. According to embodiments, module 240 predicts or computes probability distributions of predicted bicycle trajectories and predicted lean angles in time series. Alternatively or in addition, module 240 may be configured to compute a probability distribution of the bicycle's lateral position. The probability distribution defines or represents the possibility or probability of one or more events, such as the probability that the bicycle will fall over or tip over due to an excessive lean, the probability that the bicycle will oversteer (turn too sharply) or understeer (not turn enough) while turning along the predicted trajectory, the probability that the bicycle will collide with an obstacle or hazard along its predicted trajectory, the probability that the bicycle will violate local traffic rules and regulations along the predicted trajectory (e.g., illegally entering a prohibited area, riding alongside a vehicle, etc.), and probabilities of these kinds.

[0044] According to the embodiment, module 240 predicts the above probability distribution using one or more probabilistic filtering algorithms (e.g., Kalman filtering). Furthermore, bicycle information (e.g., cyclist information, bicycle type, cargo information, etc.) is used by module 240 to calculate or compute the above probability distribution. Furthermore, module 240 adjusts or fine-tunes the probability distribution based on one or more of the bicycle information. For example, module 240 adjusts the probability distribution according to the type of cyclist (e.g., increasing the probability that the bicycle will suddenly fall over based on the determination that the cyclist falls into the "child" or "elderly" category), according to the type and / or number of cargo mounted on the bicycle (e.g., widening the probability distribution of trajectory and incline angle based on the determination that multiple child seats are mounted on the bicycle), according to the type of bicycle drive (e.g., narrowing the probability distribution of trajectory and incline angle based on the determination that the bicycle is an electric assist bicycle), and similar factors.

[0045] According to the embodiment, module 240 is configured to continuously (or periodically) update trajectory prediction, tilt angle prediction, and the calculation of the probability distribution of the predicted trajectory and tilt angle based on real-time (or near real-time) information obtained from onboard sensors and surrounding vehicles, taking into account the immediate state of the bicycle and environmental conditions. Furthermore, module 210 provides information on the predicted bicycle trajectory, the predicted tilt angle of the bicycle, and / or the probability distribution of the predicted trajectory and tilt angle to other modules of the vehicle system 200 (e.g., collision avoidance module 250, transmitter module 260, etc.) for further use or processing.

[0046] Referring further to Figure 2, the collision avoidance module 250 is configured to determine the risk of collision between the vehicle and the bicycle, based on at least the predicted trajectory of the bicycle. According to one embodiment, the module 250 compares one or more of the relative positions between the vehicle and the bicycle, the speeds of the vehicle and the bicycle, and the predicted trajectories of the vehicle and the bicycle to calculate or determine the risk of collision. According to another embodiment, the module 250 dynamically assesses the risk of collision by continuously (or periodically) receiving updated bicycle trajectories from the module 240.

[0047] Furthermore, module 250 determines the collision risk by considering whether the cyclist and / or driver is in violation of (or has a probability of violating) one or more traffic rules and regulations. According to the embodiment, based on the determination that the cyclist / driver is in violation of one or more traffic rules and regulations, or that the cyclist / driver may not be familiar with traffic rules and regulations, module 250 increases the determined collision risk by a predetermined percentage. For example, module 250 increases the determined collision risk by 5% based on the determination that the cyclist may be in violation of one traffic rule, increases the determined collision risk by 10% based on the determination that the cyclist has violated one traffic rule, and does the same.

[0048] Once the risk of collision is determined, module 250 performs (or instructs other modules to perform) one or more actions to reduce the risk of collision and mitigate a potential collision between the vehicle and the bicycle.

[0049] According to the embodiment, module 250 instructs user interface (UI) module 270 to generate one or more user interfaces that provide one or more feedback to the vehicle driver according to the risk of collision and / or predicted trajectory. For example, UI module 270 generates at least one graphical user interface (GUI) that includes 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 collision risk and a second color intensity corresponding to a second level of collision risk, and includes a first icon corresponding to the first level of collision risk and a second icon corresponding to the second level of collision risk, etc.) and / or at least one audio user interface (VUI) that includes an auditory alert corresponding to the risk of collision (e.g., the auditory alert has a first volume corresponding to a first level of collision risk and a second volume corresponding to a second level of collision risk, etc.). The module 270 then provides at least one GUI to the in-vehicle visual devices (e.g., head-up display (HUD), infotainment display, navigation system display, etc.) and at least one VUI to the in-vehicle auditory devices (e.g., speaker, buzzer, etc.), thereby presenting the driver with visual and / or auditory alerts.

[0050] In one embodiment, module 250 instructs vehicle control module 280 to perform one or more vehicle control operations in accordance with the risk of collision and / or predicted trajectory. For example, vehicle control module 280 calculates the optimal steering angle and trajectory adjustments required to maintain a safe separation distance from the bicycle while maintaining vehicle stability, and then provides steering guidance to adjust the vehicle's steering wheel based on the optimal steering angle, thereby creating a virtual barrier or safety zone between the vehicle and the bicycle. In another example, vehicle control module 280 signals the vehicle's electronic control unit (ECU) to activate a vibration motor located within the steering wheel so that the vibration motor generates vibrations of the steering wheel according to each risk of collision (for example, the vibrations of the steering wheel 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.). Without departing from the scope of this disclosure, vehicle control module 280 may perform other appropriate operations, such as controlling the vehicle's brake system or throttle system, thereby intended to reduce the risk of collision and avoid a collision.

[0051] According to the embodiment, module 250 provides transmitter module 260 with information related to collision risk avoidance, such as information on the risk of collision, information on visual alerts and / or auditory alerts (obtained from UI module 270), and / or information on vehicle control (obtained from vehicle control module 280). Transmitter module 260 is configured to receive collision risk avoidance information from module 250 and information on the predicted trajectory and inclination angle of the bicycle, as well as information on their probability distributions, from module 240. Thus, transmitter module 260 transmits the above information to one or more surrounding vehicles via V2V communication and / or to devices associated with the cyclist via a cellular network.

[0052] In embodiments in which module 250 determines that a driver and / or cyclist is in violation of (or has a certain probability of in violation of) one or more traffic rules and regulations, module 250 instructs module 260 to share the information with one or more relevant users. In some embodiments, module 260 provides an alert or notification to the driver and / or cyclist associated with the violation, along with the relevant traffic rule or regulation. Alternatively, module 260 may broadcast the driver and / or cyclist information, along with the relevant traffic rule or regulation, to surrounding vehicles or bicycles.

[0053] In some embodiments, one or more of modules 210 to 280 utilize and leverage one or more artificial intelligence (AI) and / or machine learning (ML) models to perform one or more operations described herein. For example, module 220 uses one or more AI / ML models to detect and identify one or more objects from sensing data, module 240 uses one or more AI / ML models to predict or calculate the probability distribution of predicted bicycle trajectories and incline angles, module 250 uses one or more AI / ML models to determine appropriate actions to reduce collision risk, and so on. In some embodiments, module 220 uses one or more AI / ML models (e.g., semantic segmentation models) to detect the lane in which the bicycle is traveling.

[0054] Furthermore, one or more of modules 210-280 are configured to continuously (or periodically) perform one or more operations described herein based on the real-time (or near real-time) status of the bicycle, vehicle, and environmental 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 bicycle's trajectory and tilt angle, module 250 continuously predicts collision risk and determines appropriate operations associated with 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, module 260 continuously provides the latest information to one or more surrounding vehicles and cyclists, and similar operations are performed.

[0055] Next, a non-exclusive, exemplary use case (in which the operation of the vehicle system 200 is implemented) is described with reference to Figure 1. For illustrative purposes only, we assume that each of the vehicles 120-140 has a vehicle system 200 implemented internally.

[0056] Referring again to Figure 1, the vehicle system within vehicle 130 acquires environmental information about the vehicle 130 from at least one onboard sensor. The system then determines first information associated with bicycle 110 based on the environmental information. Furthermore, the system within vehicle 130 acquires second information associated with bicycle 110 from vehicle 120. The first information includes bicycle information of bicycle 110, such as the position of bicycle 110, the type of bicycle 110, the type of cyclist riding bicycle 110, and one or more of the same kind. The second information includes bicycle information determined by the system of vehicle 120 based on environmental information about the vehicle 120 (captured by one or more onboard sensors within vehicle 120), and further includes one or more of the following: information on the predicted trajectory and tilt angle of bicycle 110 by the system of vehicle 120, information on the probability distribution of the predicted trajectory and tilt angle, information on collision risk determined by the system of vehicle 120, information on alerts or warnings regarding potential collisions, and information on vehicle control performed by the system of vehicle 120.

[0057] Upon acquiring the first and second pieces of information, the system within the vehicle 130 predicts the trajectory of the bicycle 110 according to the first and second pieces of information. In some embodiments, the system in the vehicle 130 is further intended to acquire additional information associated with the bicycle 110 from other vehicles (e.g., vehicles ahead of vehicle 120, vehicles from the oncoming lane, etc.) and then predict the trajectory of the bicycle 110 based on the additional information. Furthermore, the system in the vehicle 130 further predicts the tilt angle of the bicycle 110 and calculates the probability distribution of the predicted bicycle trajectory and tilt angle.

[0058] Once the bicycle's trajectory is predicted, the system in vehicle 130 determines the risk of a collision between vehicle 130 and bicycle 110 based on the predicted trajectory.

[0059] Therefore, the system in vehicle 130 provides feedback to the driver of vehicle 130 according to the risk of collision and the predicted trajectory. For example, based on the risk of collision, the system in vehicle 130 generates steering feedback (e.g., steering wheel vibration and / or steering guidance that applies corrective steering input to move vehicle 130 away from bicycle 110 and create a virtual barrier between vehicle 130 and bicycle 110). Furthermore, based on the risk of collision, the system in vehicle 130 generates at least one GUI including a visual alert (e.g., a collision warning icon with a color corresponding to the risk of collision, a flashing sign with an intensity corresponding to the risk of collision, an animation simulating a collision, etc.), and then presents at least one GUI to the driver via a display in vehicle 130 (e.g., HUD, infotainment display, navigation system display, etc.). Furthermore, based on the risk of collision, the system in vehicle 130 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.).

[0060] According to the embodiment, the system of the vehicle 130 compares the most recent collision risk with the previous collision risk and then adjusts the intensity of the feedback based on this comparison. Specifically, the system increases the intensity of the feedback based on the determination that the most recent / current collision risk is higher than the previous collision risk (e.g., increasing the color intensity of the visual alert displayed on the display of the first vehicle, increasing the volume of the auditory alert output by the speaker of the first vehicle, increasing the vibration intensity of the steering wheel of the first vehicle, etc.). Conversely, the system decreases the intensity of the feedback based on the determination that the most recent / current collision risk is lower than the previous collision risk (e.g., decreasing the color intensity of the visual alert, decreasing the volume of the auditory alert, decreasing the vibration intensity of the steering wheel, etc.).

[0061] According to one embodiment, the system of the vehicle 130 is configured to adjust the intensity of the feedback according to a probability distribution of the bicycle. Refer to Figure 4, which shows an exemplary graph aligned in relation to the probability distribution of the bicycle and the intensity of the steering feedback according to one or more embodiments. As shown in Figure 4, the bicycle has a probability distribution relating to the lateral position of the bicycle. The probability distribution is predicted or calculated in real time (or near real time) by the system of the vehicle 130 based on first information (obtained from a sensing module and / or an object detector module) and second information (obtained from one or more external devices or external vehicles via a receiver module). In this regard, the system of the vehicle 130 may calculate or predict a steering feedback intensity distribution representing the predicted or expected steering feedback intensity at each lateral position of the bicycle, based on the probability distribution of the bicycle. Thus, the system of the vehicle 130 adjusts the real-time intensity of the steering feedback based on the intensity distribution. Without departing from the scope of this disclosure, it is intended that the system may adjust the intensity of other feedback (e.g., visual alerts, auditory alerts, steering guidance, vehicle brakes, etc.) in a similar manner.

[0062] Referring again to Figure 1, the system of vehicle 130 communicates with surrounding vehicles (e.g., vehicles 120, 140, and 150) and exchanges information with them. For example, environmental information captured by the onboard sensors of vehicle 130 includes images showing bicycle 110 and vehicle 120, and such information is useful for vehicle 120 to determine or enhance collision risk assessment and mitigation actions. Therefore, when environmental information is acquired, the system of vehicle 130 transmits the above information to vehicle 120.

[0063] Similarly, information acquired or determined by the system of vehicle 130, such as bicycle information, predicted bicycle trajectory and incline angle, probability distribution of bicycles, collision risk, and similar information, is useful to vehicles 140 and 150 because vehicles 140 and 150 do not have direct visibility of bicycle 110. Therefore, the system of vehicle 130 communicates with vehicles 140 and 150 and shares information with them. In this way, even when vehicles 140 and 150 do not have direct or clear visibility of bicycle 110, vehicles 140 and 150 can use the shared information to effectively determine the collision risk with bicycle 110 and take one or more actions in a timely manner to mitigate the collision risk.

[0064] In addition, vehicles 120-150 communicate with each other continuously (or periodically) to exchange information. In this case, vehicles 120-150 use information obtained from other vehicles to cross-reference or verify information obtained and / or determined by onboard sensors, use information obtained from one vehicle to cross-reference or verify information obtained from another vehicle, and do the same. For example, vehicle 130 uses information obtained from vehicle 120 to verify the accuracy or integrity of information obtained by onboard sensors. As another example, vehicle 140 uses information obtained from vehicle 120 to verify information obtained from vehicle 130, or uses information obtained from vehicle 130 to verify information obtained from vehicle 120, before using the acquired information to determine collision risk or before performing any action to mitigate collision risk. In this way, the accuracy of the information used to determine collision risk can be improved, thereby resulting in accurate determination of collision risk and actions to mitigate collision risk.

[0065] Furthermore, once a collision risk is determined, one or more of the vehicles 120-150 notify or warn the cyclist on bicycle 110. For example, vehicle 120 provides an alert to the cyclist on bicycle 110 regarding a potential collision between vehicle 120 (or any of the vehicles 130-150). The alert is provided via frequency modulation (FM) radio communication, amplitude modulation (AM) radio communication, cellular networks, satellite radio communication, cellular network communication (e.g., 3G, 4G, 5G, etc.), Bluetooth® communication, Wi-Fi® communication, and / or other appropriate wireless communication. In this way, the cyclist can be alerted in a timely manner about a potential collision and can take proactive action to avoid the collision (e.g., move off the road, change the bicycle's trajectory, reduce speed, etc.). Similarly, if at least one traffic rule / regulation violation (or potential violation) is determined, one or more of the vehicles 120–150 will notify or warn the relevant driver and / or cyclist so that the driver and / or cyclist may become aware of the violation and take timely action to avoid it.

[0066] Next, a description of exemplary components of a vehicle system that can be implemented in one or more vehicles to mitigate collisions with bicycles is provided. Refer to Figure 5, which shows a block diagram of exemplary components of a vehicle system 500 according to one or more embodiments. System 500 in Figure 5 corresponds to system 200 in Figure 2, and therefore, unless otherwise explicitly stated, the features described herein with respect to system 200 and system 500 are intended to be applicable to each other. Furthermore, one or more components of system 200 (e.g., modules 210-280) are implemented by one or more components of system 500.

[0067] As shown in Figure 5, 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 onboard sensor 550, at least one input / output component 560, and at least one communication interface 570. Without departing from the scope of the present disclosure, the system 500 is intended to include more or fewer components than those shown in 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 may include multiple storage components 540, and such variations are possible.

[0068] 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 Interconnection Network (LIN) bus, an Ethernet bus, and other types of buses that enable vehicle components such as components 520-570, actuators, electronic control units (ECUs), and similar items to communicate with each other in real time (or near real time).

[0069] At least one processor 520 is implemented in hardware, firmware, or a combination of hardware and software. According to embodiments, the processor 520 includes a central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), accelerated processing unit (APU), microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), and / or other types of processing or computing components. In some embodiments, the processor 520 includes one or more processors programmable to perform one or more operations described herein. Furthermore, the processor 520 may include a plurality of processing units, each specifically designed to perform a particular operation (for example, each of modules 210-280 in Figure 2 is assigned a dedicated processing unit, etc.).

[0070] At least one memory 530 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, and / or optical memory) for storing information and / or instructions used 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, along with a corresponding drive, includes hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact disks (CDs), digital multipurpose disks (DVDs), floppy disks, cartridges, magnetic tapes, and / or another type of non-temporary computer-readable media.

[0071] According to the embodiment, the storage component 540 is configured to store computer-readable or computer-executable instructions for implementing one or more modules of the system (e.g., modules 210-280), one or more AI / ML models for implementing one or more operations of modules 210-280, one or more sensing data, one or more bicycle information, one or more environmental information, one or more data acquired from an external device or other device, one or more predicted bicycle trajectories, one or more predicted bicycle inclination 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 pieces of 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 the same. The storage component 540 provides the stored information to the memory 530 for execution by the processor 520.

[0072] At least one onboard sensor 550 includes one or more sensors mounted on the vehicle, each configured to detect, measure and capture sensing data. For example, at least one sensor 550 includes an accelerometer that measures and captures data associated with the vehicle's acceleration / deceleration, vehicle speed and / or vehicle mileage; an image sensor (e.g., a camera) that detects and captures image data around or near the vehicle; a LiDAR (Light Detection and Ranging) sensor that detects and captures data associated with light from one or more light spectra such as the visible spectrum, infrared spectrum, ultraviolet spectrum and / or other light spectra; an audio sensor (e.g., a microphone) that detects and captures audio 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)) that measures and captures data associated with the vehicle's location, position and / or orientation; and parts and objects of the vehicle. The system includes contact sensors (e.g., pressure detectors, impact detectors, etc.) that detect and capture data between the vehicle and the surrounding objects (e.g., bicycles), air sensors that measure and capture data associated with the air inside and / or outside the vehicle (e.g., oxygen levels, pollution levels, humidity levels, etc.), weather sensors (e.g., rain sensors, snow sensors, wind speed sensors, visibility sensors, etc.) that measure and capture weather conditions around the vehicle (e.g., the likelihood and intensity of rainfall, the presence or absence of snow or snow cover, wind direction, the likelihood of gusts, the likelihood of fog / mist / haze, etc.), proximity sensors (e.g., ultrasonic sensors, LiDAR, radar, etc.) that measure the distance between the vehicle and surrounding objects (e.g., bicycles), FM / AM receivers that detect radio signals from road infrastructure of radio stations and thereby receive information (e.g., broadcast weather forecasts, updated traffic information, emergency alerts, local traffic rules and regulations, etc.), and other sensors suitable for deployment in the vehicle.

[0073] At least one input / output component 560 includes one or more input components (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, and / or microphone) that enable the system 500 to receive information, for example, via user input. In addition or alternatively, the input / output component 560 includes one or more output components (e.g., a display, speaker, navigation device, one or more light-emitting diodes (LEDs), etc.) that provide output information from the system 500.

[0074] At least one communication interface 570 includes a transceiver-like component (e.g., a transceiver and / or 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 wired and wireless connections. The communication interface 570 enables the system 500 to receive information from and / or provide information to one or more devices outside the vehicle (e.g., one or more devices in other vehicles, devices implemented in road infrastructure, devices on a bicycle, etc.). For example, the communication interface 570 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a Universal Serial Bus (USB) interface, a Wi-Fi interface, a cellular network interface, or similar.

[0075] 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, enabling components 520-560 to communicate with other devices outside the vehicle. Furthermore, the communication interface 570 may include one or more application programming interfaces (APIs) that enable system 500 (or one or more components included in system 500) to communicate with one or more software applications (e.g., software applications deployed on the cyclist's device, software applications implemented in the vehicle systems of surrounding vehicles, etc.). According to the embodiments, the receiver module 230 and the transmitter module 260 (or one or more operations associated therewith) are implemented by the communication interface 570.

[0076] System 500 performs one or more operations described herein in response to at least one processor 520 executing a computer-executable instruction for implementing one or more of the functional modules 210 to 280 in Figure 2. These computer-executable instructions are stored in a non-temporary computer-readable storage medium such as memory 530 and / or storage component 540. A computer-readable storage medium is defined herein as a non-temporary memory device. A memory device includes a memory space in a single physical storage device or a memory space that extends across multiple physical storage devices.

[0077] Computer executable instructions (e.g., software instructions) may be read into 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 memory 530 and / or storage component 540 cause the processor 520 to execute one or more processes described herein. In addition or alternatively, hardwired circuits may be used instead of or in combination with software instructions to execute one or more processes described herein. For this reason, the implementations described herein are not limited to any particular combination of hardware circuits and software.

[0078] Figure 6 shows a flowchart of an exemplary method 600 for mitigating a collision between a first vehicle and a bicycle in the vicinity of the first vehicle, according to one or more embodiments. Method 600 is executed by at least one processor (e.g., processor 520) of a system implemented in the first vehicle (vehicle system 200, vehicle system 500, etc.) upon execution of relevant computer instructions stored in at least one memory storage of the system (e.g., memory 530, storage component 540, etc.).

[0079] Referring to Figure 6, in operation 610, at least one processor is configured to acquire environmental information around 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., bicycles, road obstacles, other vehicles, etc.), lane information (the lane the bicycle is traveling in), weather conditions (e.g., sunny, rainy, foggy, snowy, etc.), timing information (e.g., current time, daytime, nighttime, etc.), lighting conditions (e.g., bright environment, dark environment, etc.), road conditions (e.g., slippery road, uneven surface, winding road, etc.), traffic information (e.g., congestion, local traffic rules and regulations, etc.), history of previous bicycle accidents near the point where the bicycle is traveling, and similar information. According to the embodiment, at least one onboard sensor includes a camera, and the environmental information includes at least one image captured by the camera.

[0080] When a computer-readable instruction for implementing the sensing module is executed, operation S610 is performed by at least one processor, the specific operation of which has been described above with respect to module 210 in Figure 2. For this reason, further explanation of the specific operation for acquiring environmental information is omitted below for brevity.

[0081] Once 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 bicycle's location, the bicycle's type, and the type of cyclist riding the bicycle. According to an embodiment in which the environmental information includes at least one image, at least one processor performs one or more image processing operations on at least one image to identify the object of interest (e.g., bicycle, cyclist, etc.), and then performs one or more classification / categorization operations to determine the bicycle's type, the cyclist's type, and the bicycle's location.

[0082] When a computer-readable instruction for implementing the object detector module is executed, operation S620 is performed by at least one processor, the specific operation of which has been described above with respect to module 220 in Figure 2. For this reason, for brevity, further explanation of the specific operation for detecting and extracting the first information from the environmental information is omitted below.

[0083] Referring further to Figure 6, in operation S630, at least one processor is configured to receive second information associated with the bicycle. The second information includes information obtained from the second vehicle and captured by one or more onboard sensors of the second vehicle, and information calculated based on that information. For example, the second information includes environmental information captured by the onboard sensors of the second vehicle, bicycle information obtained by the system of the second vehicle based on the relevant environmental information, information on the bicycle trajectory predicted by the system of the second vehicle, information on the bicycle tilt angle predicted by the system of the second vehicle, a probability distribution of the bicycle predicted or calculated by the system of the second vehicle, a collision risk determined by the system of the second vehicle, feedback generated by the system of the second vehicle according to the relevant collision risk and predicted trajectory, a potential collision alert / warning generated by the system of the second vehicle, a red flag indicating that the cyclist and / or the driver of the second vehicle may not be familiar with local traffic rules and regulations, and similar information. In addition, or alternatively, at least one processor may be configured to directly acquire second information from a device deployed on the bicycle (e.g., a cyclist's mobile device).

[0084] When a computer-readable instruction for implementing the receiver module is executed, operation S630 is performed by at least one processor, the specific operation of which has been described above with respect to module 230 in Figure 2. For this reason, for brevity, further explanation of the specific operation for receiving the second piece of information is omitted below.

[0085] It should be noted that without departing from the scope of this disclosure, operations S610 to S630 may be performed in any suitable order. For example, operation S630 may be performed simultaneously with operation S610 or operation S620, after operation S610 or operation S620, or before operation S610 or S620.

[0086] Having obtained the first and second pieces of information, method 600 proceeds to operation S640, in which at least one processor is configured to predict the bicycle's trajectory according to the first and second pieces of information. According to one embodiment, at least one processor further predicts the bicycle's tilt angle and calculates the probability distribution of the bicycle based on the predicted trajectory and the predicted tilt angle.

[0087] When the computer-readable instructions for implementing the trajectory predictor module are executed, operation S640 is performed by at least one processor, the specific operation of which has been described above with respect to module 240 in Figure 2. For this reason, further explanation of the specific operation for predicting the bicycle trajectory is omitted below for brevity.

[0088] Once the 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. Once computer-readable instructions for implementing the collision avoidance module are executed, operation S650 is performed by at least one processor, the specific operation of which has been described above with respect to module 250 in Figure 2. For this reason, further explanation of the specific operation for determining the collision risk is omitted below for brevity.

[0089] Once the collision risk is determined, method 600 proceeds to operation S660, in which at least one processor is configured to provide feedback to the driver of the first vehicle according to the collision risk and predicted trajectory. According to the embodiment, providing feedback includes generating steering feedback based on the collision risk. The steering feedback includes at least one of steering vibration and steering guidance away from the bicycle.

[0090] According to the embodiment, the feedback includes an alert of a potential collision between a first vehicle and a bicycle. In this regard, providing the feedback includes generating at least one GUI, which includes a visual alert, based on the risk of collision, and presenting at least one GUI to the driver via a display in the first vehicle. In addition or alternatively, providing the feedback may also include generating an audible alert, based on the risk of collision, and presenting the audible alert to the driver via a speaker in the first vehicle.

[0091] According to the embodiments, in addition to providing feedback to the driver of the first vehicle, at least one processor provides feedback to the third vehicle (e.g., a potential collision alert) and / or provides feedback to a device associated with a cyclist of a bicycle (e.g., a potential collision alert). In some embodiments, at least one processor provides feedback to the third vehicle with or without using first information (acquired in operation S620).

[0092] When a computer-readable instruction is executed to implement one or more of the UI module, vehicle control module, and transmitter module, operation S660 is executed by at least one processor, the specific operation of which has been described above with respect to modules 270, 280, and 260 in Figure 2, respectively. For this reason, further explanation of the specific operation for providing feedback is omitted below for brevity.

[0093] Once feedback is provided, method 600 is terminated or terminated. Alternatively, method 600 may return to operation S610 so that at least one processor repeats operations S610 to S660 for at least a predetermined period of time. In this way, the vehicle system continuously or repeatedly determines the risk of collision with the bicycle based on the real-time state of the bicycle and the actual environmental conditions.

[0094] The operations shown in Figure 6 are merely illustrative operations performed by the vehicle system of an exemplary embodiment, and the scope of this disclosure should not be limited thereto. Specifically, at least one processor of the system may be configured to implement or perform one or more additional operations of modules 210-280.

[0095] For example, when providing feedback to a driver and / or cyclist, at least one processor may compare the current / most recent risk of collision (determined in operation S650) with the previous risk of collision (determined in a previous operation loop), and then adjust the intensity of the feedback accordingly. For example, at least one processor may increase the intensity of the feedback (e.g., increase the color intensity of a visual alert, increase the volume of an audible alert, increase the strength of the steering wheel vibration, etc.) based on a determination that the current / most recent risk of collision is higher than the previous risk of collision. Conversely, the system may decrease the intensity of the feedback (e.g., decrease the color intensity of a visual alert, decrease the volume of an audible alert, decrease the strength of the steering wheel vibration, etc.) based on a determination that the current / most recent risk of collision is lower than the previous risk of collision. In this embodiment, the feedback presented to the driver and / or cyclist can adapt to the situation changing in real time (or near real time), thereby increasing the effectiveness of collision avoidance and collision mitigation.

[0096] Taking at least the foregoing into consideration, exemplary embodiments of this disclosure address the challenges imposed by the dynamic nature of the behavior and state of a bicycle. Specifically, exemplary embodiments enable a vehicle to capture environmental information and share the captured information with surrounding vehicles, thereby enabling collaborative sensing between vehicles in real time (or near real time). Thus, exemplary embodiments enable a vehicle to efficiently and effectively predict and prevent collisions with bicycles, particularly in scenarios where clear or direct visibility of the bicycle is unavailable.

[0097] Furthermore, drivers or cyclists unfamiliar with local traffic rules and regulations will be notified and alerted in a timely manner, enabling them to take corrective action. This further reduces the risk of collisions while avoiding violations of local traffic rules and regulations.

[0098] Furthermore, the exemplary embodiment utilizes applied 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 in accordance with changing circumstances.

[0099] To this end, the exemplary embodiment provides robust and accurate collision risk prediction in dynamic environments, enhances the effectiveness of collision avoidance maneuvers, and improves the safety of both drivers and cyclists in situations where bicycle trajectories and behavior are difficult to predict. Furthermore, the exemplary embodiment also provides human-centric safety enhancements by automatically providing appropriate feedback to relevant users, which leads to more useful predictions and improved collision avoidance when the road includes diverse users (e.g., drivers of various types of vehicles, cyclists of various backgrounds, etc.).

[0100] The features, advantages, and significance of the exemplary embodiments described above in this specification are only a part of the disclosure and are not intended to be exhaustive or to limit the scope of the disclosure. Further descriptions of the features, components, configurations, operations, and implementations of the exemplary embodiments of the disclosure, as well as the related technical advantages and technical significance, are provided below.

[0101] It should be understood that the specific order or hierarchy of blocks in the processes / flowcharts disclosed herein is an example of an exemplary approach. It should be understood that the specific order or hierarchy of blocks in the processes / flowcharts may be rearranged based on design preferences. Furthermore, some blocks may be combined or omitted. The claims of the appended methods present elements of various blocks in a sample order and do not mean to limit the user to the specific order or hierarchy presented.

[0102] Some embodiments relate to systems, methods, and / or computer-readable media in integration at any possible level of technical detail. Furthermore, as described above in this specification, one or more of the above-described components may be implemented as instructions stored in a computer-readable medium and executable by at least one processor (and / or include at least one processor). The computer-readable medium includes a non-temporary computer-readable storage medium having computer-readable program instructions thereon for causing a processor to perform an operation.

[0103] A computer-readable storage medium is a tangible device capable of holding and storing instructions used by an instruction execution device. Computer-readable storage mediums include, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage mediums includes, namely, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooved raised structures on which instructions are stored, and any suitable combination thereof. When used herein, a computer-readable storage medium should not be construed as a radio wave or other freely propagating electromagnetic wave, a waveguide or other transmission medium (e.g., an optical pulse passing through an optical fiber cable), or an electrical signal transmitted via a wire.

[0104] The computer-readable program instructions described herein 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 fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each computing / processing device.

[0105] Computer-readable program code / instructions for performing an operation may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-independent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++ or similar, and procedural programming languages ​​such as the C programming language or similar programming languages. Computer-readable program instructions may be fully executed on the user's computer, partially executed on the user's computer as a standalone software package, partially executed on the user's computer and partially on a remote computer, or fully executed on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be made to an external computer (for example, via the Internet using an Internet service provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) executes computer-readable program instructions by personalizing the electronic circuit using state information of computer-readable program instructions in order to perform an action or operation.

[0106] 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, such that the instructions, executed via the processor of a computer or the processor of another programmable data processing device, create means for implementing functions / actions specified in blocks or blocks of a flowchart and / or block diagram. These computer-readable program instructions may be stored in a computer-readable storage medium that can instruct a computer, a programmable data processing device, and / or other device to function in a particular manner, such that the storage medium containing the instructions therein comprises a product containing instructions that implements modes of functions / actions specified in blocks or blocks of a flowchart and / or block diagram.

[0107] Computer-readable program instructions may be loaded into a computer, another programmable data processing device, or another device so that instructions executed on a computer, another programmable device, or another device implement a set of operational steps on the computer, another programmable device, or another device, thereby generating a computer implementation process.

[0108] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in a flowchart or block diagram represents a module, segment, or portion of instructions comprising one or more executable instructions for implementing a specified logical function. Methods, computer systems, and computer-readable media may include additional blocks, fewer blocks, different blocks, or blocks in a different arrangement than those depicted in the figures. In some alternative embodiments, the functions described in the blocks may occur outside the order shown in the figures. For example, two consecutively shown blocks may actually be executed simultaneously or nearly simultaneously, and blocks may sometimes be executed in reverse order depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be implemented by a special-purpose hardware-based system that performs a specified function or action or a combination of special-purpose hardware and computer instructions.

[0109] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or combinations of hardware and software. The specific control hardware or software code used to implement these systems and / or methods is not limiting to the implementation. For this reason, the operation and behavior of the systems and / or methods are described herein without reference to specific software code. It will be understood that software and hardware may be designed to implement the systems and / or methods based on the descriptions herein.

Claims

1. A method to mitigate a collision between a first vehicle and a bicycle in the vicinity of the first vehicle, which is performed by at least one processor of the system in the first vehicle, The steps include acquiring environmental information around the first vehicle from at least one onboard sensor, A step of obtaining first information associated with the bicycle from the environmental information, wherein the first information includes one or more of the following: the location of the bicycle, the type of the bicycle, and the type of cyclist riding the bicycle. A step of 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. A step of predicting the trajectory and incline angle of the bicycle according to the first information and the second information, A step of determining the risk of collision between the first vehicle and the bicycle based on the predicted trajectory and inclination angle, A step of providing feedback to the driver of the first vehicle in accordance with the collision risk and the predicted trajectory. Methods that include...

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

3. The method according to claim 2, further comprising the step of providing a third vehicle with one or more of the first information and the alert for the potential collision.

4. The method according to claim 2, further comprising the step of providing an alert of the potential collision to a device associated with the cyclist of the bicycle.

5. The method according to any one of claims 1 to 4, wherein the at least one onboard 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 4, wherein the step of providing the feedback includes generating steering feedback that includes at least one of the vibration of the steering wheel and steering guidance to move away from the bicycle, based on the risk of collision.

7. The step of providing the aforementioned feedback is: Based on the aforementioned collision risk, a graphical user interface (GUI) including a visual alert is generated, Presenting the GUI to the driver via the display inside the first vehicle and The method according to any one of claims 1 to 4, including the method described in any one of claims 1 to 4.

8. The step of providing the aforementioned feedback is: To generate an audible alert based on the aforementioned collision risk, The audible alert is presented to the driver via the speaker inside the first vehicle. The method according to any one of claims 1 to 4, including the method described in any one of claims 1 to 4.

9. The steps include comparing the risk of the collision with the risk prior to the collision, The steps include increasing the intensity of the feedback based on the determination that the risk of the collision is higher than the previous risk of the collision, Based on the determination that the risk of the collision is lower than the previous risk of the collision, the steps include reducing the intensity of the feedback, The method according to any one of claims 1 to 4, further comprising:

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

11. A system for mitigating collisions between a first vehicle and a bicycle in the vicinity of the first vehicle, Memory storage that stores computer executable instructions, At least one processor communicatively coupled to the memory storage, which executes the instruction, The steps include acquiring environmental information around the first vehicle from at least one onboard sensor, A step of obtaining first information associated with the bicycle from the environmental information, wherein the first information includes one or more of the following: the location of the bicycle, the type of the bicycle, and the type of cyclist riding the bicycle. A step of 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. A step of predicting the trajectory and incline angle of the bicycle according to the first information and the second information, A step of determining the risk of collision between the first vehicle and the bicycle based on the predicted trajectory and inclination angle, A step of providing feedback to the driver of the first vehicle in accordance with the collision risk and the predicted trajectory. At least one processor that executes and A system equipped with these features.

12. The system according to claim 11, wherein the feedback includes an alert for 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 a third vehicle with one or more of the first information and the alerts for potential collisions.

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

15. The system according to any one of claims 11 to 14, wherein the at least one onboard 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 14, wherein the at least one processor is configured to provide the feedback by generating steering feedback that includes at least one of the vibration of the steering wheel and steering guidance to move away from the bicycle, based on the risk of collision.

17. The at least one processor is Based on the aforementioned collision risk, a graphical user interface (GUI) including a visual alert is generated, Presenting the GUI to the driver via the display inside the first vehicle and The system according to any one of claims 11 to 14, configured to provide the aforementioned feedback.

18. The at least one processor is To generate an audible alert based on the aforementioned collision risk, The audible alert is presented to the driver via the speaker inside the first vehicle. The system according to any one of claims 11 to 14, configured to provide the aforementioned feedback.

19. The aforementioned at least one processor further, The risk of the collision is compared with the risk prior to the collision. Based on the determination that the risk of the collision is higher than the previous risk of the collision, the intensity of the feedback is increased. The system according to any one of claims 11 to 14, configured to reduce the intensity of the feedback based on a determination that the risk of the collision is lower than the previous risk of the collision.

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

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