Method and system for intelligent driving perception

Abstract

The invention provides a method and system for intelligent driving perception, the method is based on LoRa broadcast communication, and the method comprises the following steps: adjusting an effective communication distance for intelligent driving perception; transmitting parameters of the LoRa module are adjusted; transmitting the first LoRa broadcast signal based on the transmission parameter; receiving a second LoRa broadcast signal from other vehicles; based on the effective communication distance, performing distance filtering on the received second LoRa broadcast signal, and only reserving the second LoRa broadcast signal of which the estimated sending distance is within the effective communication distance range; and providing the second LoRa broadcast signal subjected to distance filtering to an upper driving decision module for intelligent driving control. According to the technical scheme, the problem of information overload in the prior art can be solved, signal interference is reduced, communication quality is improved, behaviors of other vehicles are accurately predicted and adjusted in time, driving experience is improved, and intelligence of the automatic driving function of the vehicle is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and more specifically to a method and system for intelligent driving perception. Background Technology

[0002] Traditional autonomous driving vehicles primarily rely on onboard sensors (such as cameras, millimeter-wave radar, lidar, and ultrasonic sensors) to autonomously perceive their surroundings. They construct local environmental models by fusing multi-source sensor data to support decision-making and control. However, this "single-vehicle intelligence" model has inherent limitations: sensors are susceptible to factors such as obstructed vision, inclement weather (such as rain, fog, and snow), changes in lighting, and limitations in detection distance, making it difficult to reliably perceive blind spots, long-distance situations, or sudden traffic events.

[0003] To overcome the aforementioned bottlenecks, cooperative perception and vehicle-to-everything (V2X) technologies based on LoRa broadcast communication have been proposed, aiming to expand the perception range and improve system robustness through information sharing between vehicles and between vehicles and roadside facilities. However, existing cooperative perception solutions mostly rely on high-cost communication infrastructure or high-precision positioning and synchronization mechanisms, resulting in high deployment and maintenance costs. Furthermore, their network coverage density is low and regional fragmentation is severe, making it difficult to form continuous and effective communication links in scenarios such as non-core urban road sections, rural roads, or underground parking lots. The current cooperative perception and V2X system still cannot meet the practical requirements of intelligent driving for high reliability, low latency, and continuous perception across the entire domain in terms of cost, coverage, ease of deployment, and environmental adaptability.

[0004] In addition, LoRa broadcast communication may experience information overload when the transmission distance is long, and long-distance signals may cause interference, affecting the quality of core communication.

[0005] Therefore, a technical solution is needed to address the problem of information overload in existing technologies, reduce signal interference, improve communication quality, accurately predict the behavior of other vehicles and adjust the behavior of the vehicle in a timely manner, improve the driving experience, and realize the intelligence of the vehicle's autonomous driving function. Summary of the Invention

[0006] This invention aims to provide a method and system for intelligent driving perception, which can solve the problem of information overload in the prior art, reduce signal interference, improve communication quality, accurately predict the behavior of other vehicles and adjust the behavior of the vehicle in a timely manner, improve the driving experience, and realize the intelligence of the vehicle's autonomous driving function.

[0007] According to one aspect of the present invention, a method for intelligent driving perception is provided, the method being based on LoRa broadcast communication, the method comprising: Adjust the effective communication distance for intelligent driving perception based on the vehicle's current operating status and road conditions; Based on the effective communication distance, the transmission parameters of the LoRa module are adjusted so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance; Based on the transmission parameters, a first LoRa broadcast signal is transmitted. The first LoRa broadcast signal includes one or more of the following: the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal. Receive a second LoRa broadcast signal from other vehicles; Based on the effective communication distance, the received second LoRa broadcast signal is distance filtered, and only the second LoRa broadcast signal whose estimated transmission distance is within the effective communication distance range is retained; The distance-filtered second LoRa broadcast signal is provided to the upper-level driving decision module for intelligent driving control.

[0008] According to some embodiments, adjusting the transmission parameters of the LoRa module includes: Adjusting the transmit power level of the LoRa module adjusts the broadcast communication coverage of the LoRa module.

[0009] According to some embodiments, adjusting the transmission parameters of the LoRa module further includes: The LoRa spreading factor is adjusted by adjusting the data transmission rate level of the LoRa module, thereby adjusting the broadcast communication coverage of the LoRa module.

[0010] According to some embodiments, adjusting the transmission parameters of the LoRa module further includes: The broadcast communication coverage of the LoRa module can be adjusted by adjusting its transmission bandwidth.

[0011] According to some embodiments, distance filtering is performed on the received second LoRa broadcast signal, including: Based on the Received Signal Strength Indicator (RSSI) value of the second LoRa broadcast signal or the transmit power information carried in the second LoRa broadcast signal, the distance to the transmitting source is estimated. If the distance to the transmitting source exceeds the effective communication distance, the second LoRa broadcast signal is discarded.

[0012] According to some embodiments, the effective communication distance is determined based on at least one of the following: vehicle speed, road type, and whether the vehicle is on a curve or a road section with limited visibility.

[0013] According to some embodiments, a third LoRa broadcast signal is received from transportation infrastructure.

[0014] According to some embodiments, the third LoRa broadcast signal includes a third message encapsulation packet, which includes a roadside message or a traffic light message; The roadside message includes sign ID, type, effective range and additional information, and the type includes school zone, construction section, intersection or blind spot; The traffic light message includes the intersection ID, remaining red light time, remaining green light time, and current traffic light status.

[0015] According to another aspect of the present invention, a system for intelligent driving perception is provided, the system comprising: The communication setting module is used to adjust the transmission parameters of the LoRa module based on the effective communication distance, so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance; The first LoRa broadcast signal transmitting module is used to transmit a first LoRa broadcast signal based on the transmission parameters. The first LoRa broadcast signal includes one or more of the following: the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal. The second LoRa broadcast signal receiving module is used to receive second LoRa broadcast signals from other vehicles; The second LoRa broadcast signal filtering module performs distance filtering on the received second LoRa broadcast signal based on the effective communication distance, retaining only the second LoRa broadcast signal whose estimated transmission distance is within the effective communication distance range; The second LoRa broadcast signal uploading module provides the distance-filtered second LoRa broadcast signal to the upper-level driving decision module for intelligent driving control.

[0016] According to another aspect of the present invention, a computing device is provided, comprising: Processor; and A memory that stores a computer program, which, when executed by the processor, implements the method as described in any of the preceding methods.

[0017] According to embodiments of the present invention, the effective communication distance for intelligent driving perception is adjusted based on the current operating state of the vehicle and the road scenario. Based on this effective communication distance, the transmission parameters of the LoRa module are adjusted so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance. The vehicle transmits a first LoRa broadcast signal based on the transmission parameters and receives second LoRa broadcast signals from other vehicles. The received second LoRa broadcast signals are distance-filtered based on the effective communication distance, and the filtered second LoRa broadcast signals are provided to the upper-level driving decision module for intelligent driving control. This invention effectively solves the problem of information overload in existing technologies, reduces signal interference, improves communication quality, accurately predicts the behavior of other vehicles and adjusts the vehicle's behavior in a timely manner, improves the driving experience, and realizes the intelligence of the vehicle's autonomous driving function.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0020] Figure 1 A flowchart illustrating a method for intelligent driving perception based on LoRa communication according to an example embodiment is shown.

[0021] Figure 2 A schematic diagram illustrating the setting of LoRa transmit power levels according to an example embodiment is shown.

[0022] Figure 3 A schematic diagram illustrating the setting of LoRa data transmission rate levels according to an example embodiment is shown.

[0023] Figure 4 A schematic diagram of a LoRa-based perception system for intelligent driving, according to an example embodiment, is shown.

[0024] Figure 5 A block diagram of a computing device according to an exemplary embodiment is shown. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0026] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0029] It should be understood that although the terms first, second, third, etc., may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of the present invention. As used herein, the term "and / or" includes all combinations of any one and more of the associated listed items.

[0030] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing the present invention, and therefore cannot be used to limit the scope of protection of the present invention.

[0031] Traditional autonomous vehicles primarily rely on their onboard sensors (such as cameras, millimeter-wave radar, and lidar) for environmental perception, using multi-sensor fusion to construct a local world model to support path planning and control decisions. However, this "single-vehicle intelligence" model has significant limitations: perception capabilities are limited by line-of-sight and are susceptible to obstruction, making it difficult to detect potential road users in time in scenarios such as blind spots at intersections, curves, in adverse weather conditions, or at night, posing safety hazards.

[0032] To enhance the completeness and foresight of perception, collaborative perception and vehicle-to-everything (V2X) technologies have been widely explored, aiming to achieve beyond-line-of-sight and obstacle-crossing environmental sharing through information interaction between vehicles and roads. However, existing mainstream solutions face serious challenges: on the one hand, they rely on high-cost dedicated communication infrastructure, resulting in high deployment and maintenance costs; on the other hand, network coverage is limited and construction cycles are long, making it difficult to form effective connections in scenarios such as urban fringe areas, rural roads, tunnels, and underground parking lots, leading to fragmented and unreliable collaborative perception services.

[0033] To address this, the present invention proposes a method for intelligent driving perception that can solve the problem of information overload in existing technologies, reduce signal interference, improve communication quality, accurately predict the behavior of other vehicles and adjust the behavior of the vehicle in a timely manner, improve the driving experience, and realize the intelligence of the vehicle's autonomous driving function.

[0034] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention.

[0035] Figure 1 A flowchart illustrating a method for intelligent driving perception based on LoRa communication according to an example embodiment is shown.

[0036] See Figure 1 In S101, the effective communication distance for intelligent driving perception is adjusted according to the current operating status of the vehicle and the road scene.

[0037] According to some embodiments, the effective communication distance is determined based on at least one of the following: vehicle speed, road type, and whether the vehicle is on a curve or a road section with limited visibility.

[0038] According to some embodiments, an effective communication distance related to the vehicle's current driving decision is determined based on the driving state and road scene information. When the vehicle speed is low or the road environment is simple, the effective communication distance is set to a smaller value. When the vehicle speed is high or in scenarios with limited visibility such as curves or blind spots, the effective communication distance is correspondingly increased.

[0039] According to some embodiments, the effective communication distance can be calculated based on the safe line-of-sight distance and the vehicle response delay.

[0040] The safe visibility distance is calculated based on the vehicle's current speed using the following formula: , in, The safe field of view distance, This is the current speed of the vehicle. This is the braking response delay of the vehicle. This is the braking distance of this vehicle.

[0041] Then, the effective communication distance is calculated based on the safe field of view distance and the vehicle response delay, using the following formula: , in, The effective communication distance is... This is the current speed of the vehicle. This is the total system response delay for this vehicle. The safe field of view distance is defined as such.

[0042] In S103, based on the effective communication distance, the transmission parameters of the LoRa module are adjusted so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance.

[0043] According to some embodiments, LoRa transmission over long distances can lead to information overload, and long-distance signals may cause interference, affecting the core communication quality. Therefore, this invention solves the information overload problem and improves communication quality by dynamically controlling the broadcast distance of LoRa.

[0044] According to some embodiments, adjusting the transmission parameters of the LoRa module includes adjusting the transmission power level of the LoRa module, thereby adjusting the broadcast communication coverage of the LoRa module.

[0045] According to some embodiments, the effective communication distance is adjusted by setting LoRa transmit power levels. The higher the LoRa transmit power level, the smaller the effective communication distance, and the broadcast communication distance does not exceed a broadcast communication distance threshold. The LoRa transmit power levels include first-level transmit power, second-level transmit power, third-level transmit power, fourth-level transmit power, fifth-level transmit power, sixth-level transmit power, seventh-level transmit power, and eighth-level transmit power. The broadcast communication distance threshold is set to 1000 meters.

[0046] According to some embodiments, setting the transmit power level can directly reduce the output power of the RF front-end, thereby narrowing the effective communication range. This method is simple to operate, adjustable in real time, and suitable for scenarios requiring dynamic adjustment of communication distance. LoRa transmit power levels are typically set to 0-7 (see...). Figure 2 For a comparison of the power levels and output power corresponding to the 470MHz band, please refer to Table 1.

[0047] Table 1

[0048] According to some embodiments, adjusting the effective communication distance by setting the LoRa transmit power level is simple, requiring only the addition of a power level setting to the device firmware. The power level can be dynamically adjusted based on environmental requirements during device initialization or communication, avoiding unnecessary signal coverage and reducing overall power consumption. For example, a higher power level can be set on mountain roads to cover 500-1000 meters; a medium power level on urban roads to cover 200-300 meters; and a very low power level in parking lots to cover 50 meters.

[0049] According to some embodiments, adjusting the transmission parameters of the LoRa module further includes: adjusting the LoRa spreading factor by adjusting the data transmission rate level of the LoRa module, thereby adjusting the broadcast communication coverage of the LoRa module; and adjusting the transmission bandwidth of the LoRa module, thereby adjusting the broadcast communication coverage of the LoRa module.

[0050] According to some embodiments, the LoRa spreading factor is adjusted by setting the LoRa data transmission rate level to ensure the anti-interference capability of the effective communication distance. The higher the LoRa data transmission rate level, the smaller the LoRa spreading factor.

[0051] According to some implementations, bandwidth is the third most crucial and configurable radio frequency parameter in LoRa, after transmit power and spreading factor. It, along with spread frequency (SF) and code rate, determines the actual data transmission rate. Common LoRa bandwidth settings are 125kHz, 250kHz, and 500kHz. A larger bandwidth results in a faster data rate and reduces transmission time.

[0052] According to some implementations, with the same LoRa spreading factor (SF), doubling the bandwidth almost doubles the data rate and halves the air time for transmitting the same data packets. Conversely, with the same bandwidth, increasing the SF by one level significantly reduces the data rate and greatly increases the transmission time.

[0053] According to some implementations, the LoRa spreading factor (SF) is positively correlated with communication distance and interference immunity, and negatively correlated with data transmission rate. Reducing the SF can significantly improve the rate, but it will weaken the receiver sensitivity and propagation distance. When the LoRa data transmission rate level (DR) is set to 5, corresponding to SF=7 and bandwidth=125kHz, this represents the highest rate configuration with the same bandwidth, but also the shortest communication distance (see [link]). Figure 3 The relationship between data rate and spreading factor is shown in Table 2.

[0054] Table 2

[0055] According to some implementations, it is recommended to use SF=7 in close-range scenarios with minimal interference, and to use mechanisms such as forward error correction (FEC) to ensure data integrity. It is important to note that Adaptive Data Rate (ADR) must be disabled when modifying the DR value; otherwise, ADR will adaptively adjust the DR.

[0056] According to some embodiments, upon receiving an emergency braking signal, a sharp steering signal, or a vehicle control signal, the LoRa module immediately increases its transmit power, reduces its spread spectrum factor, and / or increases its bandwidth, thereby improving the data transmission rate.

[0057] According to some embodiments, the method of the present invention actively introduces attenuation to achieve controllable signal energy loss. It is connected in series in the RF link. Type-T or T-type resistor attenuators can achieve controllable signal energy loss. The method of this invention is based on mature passive network theory and possesses good broadband characteristics and impedance matching. It has the ability to maintain a stable attenuation value, unaffected by frequency drift. See Table 3 for standard resistor attenuation network parameters.

[0058] Table 3

[0059] In S105, based on the transmission parameters, a first LoRa broadcast signal is transmitted. The first LoRa broadcast signal includes one or more of the following: the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal.

[0060] According to some embodiments, the first LoRa broadcast signal includes a first message encapsulation packet. The first message encapsulation packet is continuously broadcast via LoRa, and includes the vehicle ID, vehicle position (longitude, latitude), speed, heading angle, acceleration, turn signal status, vehicle length / width, transmission timestamp, etc.

[0061] According to some embodiments, the vehicle obtains its position, speed, and heading angle via GPS or IMU, encapsulates them into a first message packet, and continuously broadcasts it via LoRa.

[0062] In S107, a second LoRa broadcast signal is received from other vehicles.

[0063] According to some embodiments, the second LoRa broadcast signal includes a second message encapsulation packet. The data content of the second message encapsulation packet includes: the vehicle ID of other vehicles, vehicle location (longitude, latitude), speed, heading angle, acceleration, turn signal status, vehicle length / width, transmission timestamp, etc. The data in both the first and second message encapsulation packets is approximately 50 to 100 bytes, which is well-suited for communication via LoRa, whose communication frequency is typically 1-2 times per second.

[0064] In S109, based on the effective communication distance, the received second LoRa broadcast signal is distance filtered, and only the second LoRa broadcast signal whose estimated transmission distance is within the effective communication distance range is retained.

[0065] According to some embodiments, the distance to the transmitting source is estimated based on the Received Signal Strength Indicator (RSSI) value of the second LoRa broadcast signal or the transmit power information carried in the second LoRa broadcast signal. If the distance to the transmitting source exceeds the effective communication distance, the second LoRa broadcast signal is discarded.

[0066] According to some embodiments, the distance to the transmitting source is estimated based on the Received Signal Strength Indicator (RSSI) value. If the distance to the transmitting source exceeds the effective communication distance threshold, the corresponding message is discarded.

[0067] In some implementations, dual distance verification is achieved by combining RSSI and timestamps, setting a distance threshold (e.g., 500m), and automatically discarding signals beyond the range. Signals at close range (e.g., within 300m) are assigned a higher processing priority.

[0068] In S111, the distance-filtered second LoRa broadcast signal is provided to the upper-level driving decision module for intelligent driving control.

[0069] According to some embodiments, the vehicle receives a third LoRa broadcast signal from traffic infrastructure. The third LoRa broadcast signal includes a third message encapsulation packet, which includes a roadside message or a traffic light message. The roadside message includes a sign ID, type, effective range, and additional information; the type includes school zone, construction zone, intersection, or blind spot. The traffic light message includes an intersection ID, remaining red light time, remaining green light time, and current traffic light status.

[0070] According to some embodiments, the third message encapsulation packet is continuously broadcast via LoRa, and the third message encapsulation packet includes roadside messages and traffic light messages; the roadside messages include sign ID, type, effective range and additional information, the type including school zone, construction section, intersection or blind spot; the traffic light messages include intersection ID, remaining red light time, remaining green light time and current traffic light status.

[0071] According to some implementations, LoRa is used to obtain road condition information and traffic light status of key landmarks, intersections, bridges, and tunnel entrances.

[0072] According to some embodiments, the filtered second LoRa broadcast signal in S109 is provided to the upper-level decision module, which then performs intelligent driving control based on the data in the second and third message encapsulation packets and the vehicle's own status.

[0073] According to some embodiments, the vehicle's status data is obtained, and the vehicle's position (x, y) in the global coordinate system is obtained in real time through the vehicle's positioning system. ego ,y ego ), heading angle θ ego and speed v ego ; Parse the second message encapsulation packet to extract the global location (x) of other vehicles from the received LoRa broadcast message. other ,y other ), speed magnitude v other and its heading angle θ other .

[0074] According to some embodiments, the relative position (Δx, Δy) is calculated as follows: Δx = x other -x ego , △y=y other -y ego Rotate the coordinate system, and rotate the relative position and velocity vectors in the opposite direction around the vehicle's heading angle, transforming them into a local coordinate system with the vehicle as the origin and the front as the X-axis (x... local ,y local Similarly, by performing a rotation transformation on the velocity vector, we obtain v in the local coordinate system. x,local v y,local .

[0075] According to some embodiments, data in the second and third message encapsulation packets are parsed and combined with vehicle coordinate data, vehicle speed, etc., from the first message encapsulation packet to obtain fused data. For example: the current traffic light is red, the vehicle is 200 meters away from the traffic light, there is already one vehicle waiting at the intersection, the vehicle length is 4 meters, the current speed is 40 km / h, the traffic light will turn green in 30 seconds, and there are no vehicles within 200 meters behind.

[0076] According to some embodiments, based on the fused data, trajectory prediction and intent recognition algorithms are used to infer the future driving trajectory of a vehicle and adjust the vehicle accordingly. For example, if the fused data is "the current traffic light is red, the vehicle is 200 meters away from the traffic light, there is already one vehicle waiting at the intersection, the vehicle length is 4 meters, the current speed is 40 km / h, the traffic light will turn green in 30 seconds, and there are no vehicles within 200 meters behind," then the vehicle speed can be adjusted to uniformly reduce to 20 km / h, allowing it to pass through the intersection smoothly without stopping and waiting.

[0077] According to some embodiments, fused multi-source information (including the position, speed, acceleration, and heading angle of other vehicles, as well as traffic light status, lane structure, and congestion conditions) is used as input to the prediction model. Classification models (such as logistic regression, support vector machines, or lightweight neural networks) or sequence models (such as LSTM and Transformer) are used to analyze the vehicle's current and historical motion state and environmental context. After analysis, other driving intentions are determined; these intentions are typically represented as discrete categories to guide subsequent trajectory generation.

[0078] According to some embodiments, based on the identified intent, trajectory generation models (such as Gaussian processes, LSTM encoder-decoders, Social GANs, or spatiotemporal prediction models based on graph neural networks) are used to predict the possible driving trajectories of other vehicles in the next few seconds (e.g., 3–5 seconds).

[0079] According to some embodiments, if a high-probability trajectory is predicted, the vehicle's behavior is decided and planned. Combined with safety constraints, the vehicle's speed, steering, or path is dynamically adjusted, such as "keeping straight", "preparing to turn left", "changing lanes", or "emergency braking", to achieve safe and efficient cooperative driving.

[0080] For example, LoRa traffic light nodes are installed at each intersection, and LoRa RSUs (relay units, covering a radius of 50-300m) are deployed on the roadside. When vehicle A approaches the intersection, it broadcasts a left-turn message. Vehicle B, in the blind spot, learns of vehicle A's intention through the RSU's relay. The traffic light node broadcasts "RED in 3s," causing the vehicle to slow down in advance.

[0081] According to some implementations, based on the current state (position, speed, heading) and intent label, the corresponding kinematic model is selected, or a pre-trained lightweight neural network is invoked to output a sequence of trajectory points for the next 3–5 seconds. Combined with traffic light constraints, the "remaining red light time" in the traffic light message is used as a hard constraint: if the red light duration is greater than the predicted arrival time, the target is forced to decelerate to zero before the stop line; during the green light phase, passage through the intersection area is permitted.

[0082] According to some embodiments, the planned trajectory of the vehicle is spatiotemporally overlapped with the predicted trajectories of all cooperatively perceived targets. If a potential conflict exists, response logic is triggered. For example, if the predicted trajectory of another vehicle indicates that it is about to enter the lane, a "decelerate" command is generated; if another vehicle is about to stop at an intersection and the vehicle is approaching, a "maintain current speed" or "pass slowly" command is generated; if another vehicle has a high risk of running a red light and the vehicle is in the green light phase, an "emergency stop" command is generated.

[0083] According to some embodiments, the decision results are converted into longitudinal control commands (such as target acceleration and target speed) and sent to the vehicle control unit (VCU) or ADAS controller to achieve automatic adjustment of the vehicle's behavior.

[0084] According to some embodiments, the LoRa used in the invention method does not replace the main sensors (cameras, LiDAR) and high-bandwidth communication technologies (such as 5G-V2X for transmitting high-definition maps), but it can form a strong complement to them, building a more redundant, reliable, and comprehensive perception system. For example, when visual sensors fail in inclement weather, or when the vehicle in front is "unseen" due to being blocked by a large vehicle, receiving direct status information of the vehicle in front via LoRa P2P will become an important safety barrier.

[0085] According to some embodiments, the LoRa P2P communication used in the method of the present invention is a valuable, low-cost, and low-latency auxiliary perception means for intelligent driving perception. In particular, it abandons high performance in order to pursue wide coverage, high reliability and low cost in the transmission of key state information with small data volume. It has great potential in specific application scenarios and is a valuable path to promote the commercialization of intelligent driving.

[0086] This invention dynamically controls the broadcast distance of LoRa, enabling vehicles and infrastructure to share each other's state information, thereby constructing a more comprehensive perception model. LoRa broadcasts basic safety information such as "who I am, where I am, and where I am going," effectively solving the problem of information overload in existing technologies, reducing signal interference, and improving communication quality. Combined with data fusion and prediction, it accurately predicts the behavior of other vehicles and adjusts its own behavior in a timely manner, improving the driving experience and realizing the intelligence of autonomous driving functions.

[0087] Figure 4 A schematic diagram of a LoRa-based communication system for intelligent driving perception, according to an example embodiment, is shown.

[0088] See Figure 4The LoRa-based sensing for intelligent driving includes: a communication setting module 01, a transmission parameter adjustment module 02, a first LoRa broadcast signal transmitting module 03, a second LoRa broadcast signal receiving module 04, a second LoRa broadcast signal filtering module 05, and a second LoRa broadcast signal uploading module 06.

[0089] According to some embodiments, the communication setting module 01 is used to adjust the effective communication distance for intelligent driving perception based on the current operating status of the vehicle and the road scene; the transmission parameter adjustment module 02 is used to adjust the transmission parameters of the LoRa module based on the effective communication distance, so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance; the first LoRa broadcast signal transmission module 03 is used to transmit a first LoRa broadcast signal based on the transmission parameters, the first LoRa broadcast signal including one or more of the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal; the second LoRa broadcast signal receiving module 04 is used to receive second LoRa broadcast signals from other vehicles; the second LoRa broadcast signal filtering module 05 performs distance filtering on the received second LoRa broadcast signals based on the effective communication distance, retaining only the second LoRa broadcast signals whose estimated transmission distance is within the effective communication distance range; the second LoRa broadcast signal uploading module 06 provides the distance-filtered second LoRa broadcast signals to the upper-level driving decision module for intelligent driving control.

[0090] According to some embodiments, the system also includes a third LoRa broadcast signal receiving module, which is used to synchronously receive third message encapsulation packets from roadside infrastructure (such as smart traffic lights, road sensing nodes) within the effective communication distance set by the communication setting module 01. The packets include current road conditions (congestion, accidents, construction, etc.) and traffic light status (red and green light phases, countdowns, etc.).

[0091] According to some embodiments, the communication setting module 01 sets an effective communication distance to ensure that it can receive information sent by surrounding vehicles and roadside units (such as traffic lights and road condition sensors) within the effective communication distance. The transmission parameter adjustment module 02 adjusts the transmission parameters according to the effective communication distance set by the communication setting module 01, so that the broadcast communication coverage range conforms to the effective communication distance. The first LoRa broadcast signal transmitting module 03 transmits a first LoRa broadcast signal to the surrounding area in real time to broadcast the vehicle's status. The second LoRa broadcast signal receiving module 04 receives second message packets broadcast from other vehicles within the effective communication distance set by the communication setting module 01 in real time, which contain status information such as the position, speed, direction, and acceleration of nearby vehicles. The second LoRa broadcast signal filtering module 05 filters the second LoRa broadcast signal received in real time by the second LoRa broadcast signal receiving module 04, discarding low-quality signals and data. Finally, the filtered signal is provided by the second LoRa broadcast signal uploading module 06 to the upper-level driving decision module for intelligent driving control.

[0092] The following describes an embodiment. Vehicle A approaches an intersection, but its onboard sensors (camera, radar) have limited field of view. Vehicle A's LoRa module continuously monitors the channel and receives a first message packet and a third message packet including the current road conditions broadcast from Vehicle B (on the cross road). Vehicle A parses Vehicle B's ID, precise location, speed, and heading. The intelligent driving perception system predicts based on the movement trajectories of both vehicles. If a collision risk is predicted, a safe deceleration or stopping strategy is calculated. If the predicted path is conflict-free, the vehicle maintains its current speed and proceeds. Simultaneously, Vehicle A also broadcasts its own status (the third message packet) in its own time slot, allowing Vehicle B to make corresponding decisions.

[0093] According to some embodiments, Low Power Wide Area Network (LoRa) offers a highly promising technological path. LoRa boasts advantages such as long-range transmission (up to several kilometers), low power consumption, strong penetration capabilities, and low-cost hardware, enabling the construction of lightweight, self-organizing vehicle-to-infrastructure (V2I) communication networks without the need for cellular network support. By integrating LoRa modules into vehicles and roadside nodes (such as traffic lights and signs), basic safety messages (such as location, speed, and steering status) and traffic event information (such as traffic light phases and construction warnings) can be efficiently broadcast, achieving basic cooperative sensing capabilities with wide-area coverage.

[0094] This invention combines LoRa technology to construct a low-cost, easy-to-deploy, and widely covered lightweight collaborative perception system for intelligent driving. It breaks through the perception boundaries of traditional single-vehicle intelligence while avoiding high cost and economic bottlenecks, providing practical technical support for the large-scale deployment of intelligent driving.

[0095] Figure 5A block diagram of a computing device according to an exemplary embodiment of the present invention is shown.

[0096] like Figure 5 As shown, the computing device 30 includes a processor 12 and a memory 14. The computing device 30 may also include a bus 22, a network interface card 16, and an I / O interface 18. The processor 12, memory 14, network interface card 16, and I / O interface 18 can communicate with each other via the bus 22.

[0097] Processor 12 may include one or more general-purpose CPUs (Central Processing Units), microprocessors, or application-specific integrated circuits, for executing relevant program instructions. According to some embodiments, computing device 30 may also include a high-performance display adapter (GPU) 20 for accelerating processor 12.

[0098] Memory 14 may include a machine system readable medium in the form of volatile memory, such as random access memory (RAM), read-only memory (ROM), and / or cache memory. Memory 14 is used to store one or more programs containing instructions, as well as data. Processor 12 may read the instructions stored in memory 14 to perform the methods described above according to embodiments of the present invention.

[0099] The computing device 30 can also communicate with one or more networks via a DPU smart network interface card (NIC). The DPU smart NIC is used for data processing or external communication, and the central processing unit (CPU) is used for processing data scheduled by the DPU smart NIC. The DPU smart NIC includes a root system-on-a-chip (SoC) and multiple interfaces, through which the SoC performs data communication. The SoC includes a processor and a memory, on which a computer program is stored. When the processor runs the computer program stored in the memory, it implements the method according to an embodiment of the present invention.

[0100] Bus 22 can include address bus, data bus, control bus, etc. Bus 22 provides a path for exchanging information between components.

[0101] It should be noted that, in specific implementations, the computing device 30 may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the device described above may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0102] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), network storage devices, cloud storage devices, or any type of medium or device suitable for storing instructions and / or data.

[0103] This invention also provides a computer program product comprising a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0104] Those skilled in the art will clearly understand that the technical solutions of the present invention can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware capable of independently performing or cooperating with other components to perform a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit, etc.

[0105] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention.

[0111] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0112] Exemplary embodiments of the present invention have been specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, arrangements, or implementations described herein; rather, the present invention is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended provisions.

Claims

1. A method for perception in intelligent driving, characterized in that, The method is based on LoRa broadcast communication, and the method includes: Adjust the effective communication distance for intelligent driving perception based on the vehicle's current operating status and road conditions; Based on the effective communication distance, the transmission parameters of the LoRa module are adjusted so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance; Based on the transmission parameters, a first LoRa broadcast signal is transmitted. The first LoRa broadcast signal includes one or more of the following: the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal. Receive a second LoRa broadcast signal from other vehicles; Based on the effective communication distance, the received second LoRa broadcast signal is distance filtered, and only the second LoRa broadcast signal whose estimated transmission distance is within the effective communication distance range is retained; The distance-filtered second LoRa broadcast signal is provided to the upper-level driving decision module for intelligent driving control.

2. The method according to claim 1, characterized in that, Adjusting the transmission parameters of the LoRa module includes: Adjusting the transmit power level of the LoRa module adjusts the broadcast communication coverage of the LoRa module.

3. The method according to claim 2, characterized in that, Adjusting the transmission parameters of the LoRa module further includes: The LoRa spreading factor is adjusted by adjusting the data transmission rate level of the LoRa module, thereby adjusting the broadcast communication coverage of the LoRa module.

4. The method according to claim 3, characterized in that, Adjusting the transmission parameters of the LoRa module further includes: The broadcast communication coverage of the LoRa module can be adjusted by adjusting its transmission bandwidth.

5. The method according to claim 1, characterized in that, Distance filtering is performed on the received second LoRa broadcast signal, including: Based on the Received Signal Strength Indicator (RSSI) value of the second LoRa broadcast signal or the transmit power information carried in the second LoRa broadcast signal, the distance to the transmitting source is estimated. If the distance to the transmitting source exceeds the effective communication distance, the second LoRa broadcast signal is discarded.

6. The method according to claim 1, characterized in that, The effective communication distance is determined based on at least one of the following: vehicle speed, road type, and whether the vehicle is on a curve or a road section with limited visibility.

7. The method according to claim 1, characterized in that, Also includes: Receives third LoRa broadcast signals from transportation infrastructure.

8. The method according to claim 7, characterized in that, The third LoRa broadcast signal includes a third message encapsulation packet, which includes a roadside message or a traffic light message. The roadside message includes sign ID, type, effective range and additional information, and the type includes school zone, construction section, intersection or blind spot; The traffic light message includes the intersection ID, remaining red light time, remaining green light time, and current traffic light status.

9. A system for intelligent driving perception, characterized in that, The system is based on LoRa broadcast communication, and the system includes: The communication setting module is used to adjust the effective communication distance for intelligent driving perception based on the current operating status of the vehicle and the road scene. The transmission parameter adjustment module is used to adjust the transmission parameters of the LoRa module based on the effective communication distance, so that the broadcast communication coverage of the LoRa module does not exceed the effective communication distance; The first LoRa broadcast signal transmitting module is used to transmit a first LoRa broadcast signal based on the transmission parameters. The first LoRa broadcast signal includes one or more of the following: the vehicle's position, speed, heading angle, acceleration, turn signal status, and emergency braking signal. The second LoRa broadcast signal receiving module is used to receive second LoRa broadcast signals from other vehicles; The second LoRa broadcast signal filtering module performs distance filtering on the received second LoRa broadcast signal based on the effective communication distance, retaining only the second LoRa broadcast signal whose estimated transmission distance is within the effective communication distance range; The second LoRa broadcast signal uploading module provides the distance-filtered second LoRa broadcast signal to the upper-level driving decision module for intelligent driving control.

10. A computing device, characterized in that, include: processor; as well as A memory storing a computer program that, when executed by the processor, implements the method as described in any one of claims 1-8.

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