Road accident prevention system

Abstract

FIELD: road construction.SUBSTANCE: invention relates to a system for preventing road traffic accidents. The system is implemented by placing devices on road signs and traffic lights that require vehicles to reduce speed or stop, which send coded information about the established traffic restrictions in the direction of their coverage area. Each vehicle is equipped with a receiver with a decoder and a controller. The controller acts on the vehicle's controls. The controller is also connected to a computing unit, to which the outputs of the optical visibility sensor from the driver's seat, the sensor that determines the vehicle's load level, and the sensor that determines the type and condition of the surface, based on a neural network model, are connected.EFFECT: improvement of road safety.1 cl, 1 dwg

Description

[0001] The present invention relates to the field of automobile transport, in particular to its safe operation.

[0002] Such an analogue is known as the "Vehicle Control System" (Patent for Invention of the Russian Federation No. 2855538, IPC B62J 27 / 00, B62M 6 / 50, B60W 40 / 02, G05D 101 / 00, published on 02.02.2026). The vehicle contains an engine and a vehicle control device. The engine drives at least one propeller. The vehicle control device contains a processor and memory. The memory contains a program code. The program code causes the processor to perform the vehicle control method. When determining the permissible speed of the vehicle, a control signal is generated to determine the permissible speed of the vehicle for a situation frame. The situation frame is obtained by means of a vehicle equipped with a situation sensor. During the data collection process, a control signal is generated for the data collection device. The control signal contains a command to ensure an effect on the collected raw data.The environment sensor is moved through space by a vehicle to collect data. A control signal is generated when the vehicle's speed does not correspond to the maximum permissible speed for the vehicle on the corresponding section of the route. The distance to the obstacle is selected such that the permissible speed corresponds to the maximum permissible speed for the vehicle on the corresponding section of the route.

[0003] The disadvantage of this solution is that it speeds up the preparation of data for machine learning and the subsequent determination of the permissible speed through a neural network model, but there is no mechanism for forcibly limiting the actual speed of a road vehicle, and the permissible speed is calculated based on arbitrary “situation frames”, but not on the objective visibility range determined by a physical method.

[0004] Also known is a similar "Centralized Speed ​​Control System for Road Vehicles" (Patent for Invention of the Russian Federation No. 2751133, IPC B60W 10 / 10, B60W 50 / 12, G08G 1 / 017, G08G 1 / 04, G08G 1 / 0968, G08G 1 / 16, published on 07 / 08 / 2021), including a computer control center, a system for determining the location of vehicles in real time and a system for two-way data transmission between vehicles and a computer control center, characterized in that the claimed system controls a constantly changing fleet of vehicles equipped with devices that allow you to control the speed of the vehicle remotely, as well as in that it controls only the speed of the vehicle within the limits limited by the driver, and the trajectory of movement is determined by the driver of the vehicle.

[0005] The disadvantage of this solution is the centralized road transport infrastructure, which requires the installation of stationary receivers, transmitters, and a computing center at every section of the road network. All road vehicles must be equipped with a receiver, transmitter, and actuator. It does not limit the actual speed to the maximum permissible speed, taking into account road conditions, but allows the driver to set their own speed.

[0006] The closest in technical features and chosen as a prototype is the "System for preventing road traffic accidents" (Patent for invention of the Russian Federation No. 2859604, IPC B60W 50 / 12, published on 07.04.2026) due to the equipment of road signs and traffic lights requiring a reduction in speed or a stop of vehicles, with devices that send encoded information about the established traffic restrictions in the direction of their coverage area; a receiver of the said information with a decoder and a controller that acts on the controls is installed on each vehicle; the controller, influencing the controls, will forcibly reduce the speed of the vehicle to the parameters prescribed by the sign, or completely stop it in the case of a signal prohibiting movement, regardless of the actions of the driver.

[0007] The specified solution actually implements a forced speed limit based on the maximum permitted speed limit set on a given section of the road network by a road sign; the system does not allow the actual speed to be limited to the maximum permitted speed based on road conditions, while the maximum permitted speed regulated by the road sign may be higher than the maximum permitted speed.

[0008] The objective of the technical solution is to create a system that can forcibly limit the actual speed of a road vehicle to the maximum permitted or to the permissible speed, based on actual road conditions, in order to achieve improved road safety.

[0009] The technical result of the claimed system is to improve road safety.

[0010] The claimed technical result is achieved by the fact that in a system for preventing road traffic accidents by equipping road signs and traffic lights requiring a reduction in speed or a stop of vehicles with devices that send encoded information about the established traffic restrictions in the direction of their coverage area; a receiver of said information with a decoder and a controller that acts on the controls is installed on each vehicle; moreover, the system is also equipped with a computing unit, to the inputs of which the outputs of an optical visibility sensor from the driver's seat, a sensor that determines the vehicle load level, and a sensor that determines the type and condition of the surface based on a neural network model are connected, and the output of the computing unit is connected to the input of the controller.

[0011] Providing the claimed system with a computing unit, to the inputs of which the outputs of the optical visibility sensor from the driver's seat, the sensor that determines the vehicle load level, and the sensor that determines the type and condition of the road surface based on a neural network model are connected, and the output of the computing unit is connected to the input of the controller, makes it possible to reduce the number of accidents and the severity of their consequences in conditions of poor visibility (rain, fog, snow), as well as the type and condition of the road surface due to the forced limitation of the speed of movement to the value of a safe speed of movement in the event of a violation of the safe speed limit by the driver.

[0012] The entire system operates in real time, providing an instant response to changes in the road situation, which is especially critical in conditions of rapidly changing visibility when driving in fog, heavy rain or snow.

[0013] In fog or heavy rain, when visibility is significantly reduced, the claimed system automatically adjusts engine performance via the controls, gently limiting fuel supply and reducing speed to a value appropriate to the actual situation. If the driver attempts to maintain a higher speed, the system gradually increases the limiting effect—first warning with visual and audible signals, then transitioning to active intervention with a gradual reduction in traction. In emergency situations, such as suddenly entering a zone of zero visibility, an emergency mode is activated, which turns on the hazard warning lights and initiates smooth but intense braking to a complete stop, if necessary for safety.

[0014] The device's unique feature is its adaptability to changing conditions—the system constantly recalculates the permissible speed based on visibility changes, which is especially important when driving through areas with variable fog density or uneven snow cover. Integration with a sensor that determines the type and condition of the road surface using a neural network model allows for consideration of not only visibility but also the condition of the road surface—the presence of water, ice, or slush—which further adjusts safe speed calculations. Integration with a sensor that determines the vehicle's load level allows for adjusted deceleration based on the vehicle's actual weight.

[0015] The drawing shows a diagram of the claimed system for preventing road accidents.

[0016] The system for preventing road traffic accidents is a device 1 installed on road signs or traffic lights, sending coded information about established traffic restrictions in the direction of their coverage area, a receiver 2 of said information installed on a vehicle, a decoder 3, a controller 4, controls 5, a computing unit 6, a sensor 7 for optical visibility of the road from the driver's seat, a sensor 8 that determines the vehicle load level, a sensor 9 that determines the type and condition of the surface, based on a neural network model.

[0017] The traffic accident prevention system operates as follows.

[0018] Devices 1 installed on road signs and traffic lights and sending coded information in the direction of their coverage area are perceived by receiver 2 and transmitted to decoder 3, which sends a signal to controller 4 about the need to influence controls 5 in order to forcibly reduce the speed of the vehicle to the values ​​​​prescribed by the sign, or completely stop it in the case of a signal prohibiting movement, regardless of the actions of the driver, controller 4 also receives a signal from computing unit 6, which calculates the maximum permissible speed of movement, based on the values ​​​​of sensor 7 of optical visibility of the road from the driver's workplace, sensor 8, determining the level of loading of the vehicle, sensor 9, determining the type and condition of the surface, based on the neural network model.

[0019] In fog or heavy rain, when visibility is sharply reduced, the claimed system automatically adjusts engine operation via controls 5, gently limiting fuel supply and reducing speed to a value appropriate to the actual situation. If the driver attempts to maintain a higher speed, the system gradually increases the limiting effect—first warning with visual and audible signals, then switching to active intervention with a gradual reduction in traction. In emergency situations, such as suddenly entering a zone of zero visibility, an emergency mode is activated, which turns on the hazard warning lights and initiates smooth but intense braking to a complete stop, if necessary for safety.

[0020] Integration with sensor 8, which determines the vehicle loading level, allows for the established deceleration to be adjusted depending on the actual weight of the vehicle.

[0021] Integration with Sensor 9, which determines the type and condition of the road surface based on a neural network model, allows for consideration of not only visibility but also the condition of the road surface—the presence of water, ice, or slush—which further adjusts safe speed calculations.

[0022] Calculating the maximum permissible speed of a vehicle based on the visibility of road elements . is based on the formula [Chava I. I. Forensic automotive examination. Investigation of the circumstances of a road traffic accident: a teaching aid / I. I. Chava. - Moscow: Institute for Advanced Training of the Russian Federal Center for Forensic Expertise, 2007. - 96 p.]:

[0023]

[0024] where V в – maximum permissible speed of a vehicle under conditions of visibility of road elements, km / h;

[0025] 3.6 – conversion coefficient from m / s to km / h;

[0026] j a – steady deceleration of a vehicle when braking on a horizontal section, m / s 2 ;

[0027] S в – visibility range of road elements, m;

[0028] T – total time of driver reaction and braking system response, sec.

[0029] Visibility of road elements S в – the value obtained from the optical visibility sensor 7 from the driver’s seat.

[0030] Steady-state deceleration of a vehicle when braking on a horizontal section j ais a variable value that depends on the vehicle category, vehicle loading level and the value of the adhesion coefficient φ for different types and conditions of the road surface. For their numerical determination, the average values ​​​​given in the source are used [Grigoryan, V. G. Application of braking parameters of motor vehicles in expert practice: Methodological recommendations for experts / V. G. Grigoryan, A. V. Grigoryan. - Moscow: Russian Federal Center for Forensic Expertise under the Ministry of Justice of the Russian Federation, 2021. - 40 p. - ISBN 978-5-91133-224-2. - DOI 10.30764 / 978-5-91133-224-2-2021-3. - EDN DAPGEL.], which provides the values ​​of steady-state deceleration for various vehicle categories (passenger cars, trucks, buses), load ranges (readings from sensor 8, which determines the vehicle loading level) and the values ​​of the adhesion coefficient φ for various types and conditions of road surfaces (readings from sensor 9, which determines the type and condition of the surface based on a neural network model). Specific values ​​of j. a (taking into account the vehicle category, the adhesion coefficient φ and the vehicle load) are selected by the computing unit 6 based on the closest correspondence to the current conditions.

[0031] The calculation of the total driver reaction time and braking system response time T is based on the formula [Chava I. I. Forensic automotive examination. Investigation of the circumstances of a road traffic accident: a teaching aid / I. I. Chava. - Moscow: Institute for Advanced Training of the Russian Federal Center for Forensic Expertise, 2007. - 96 p.]:

[0032]

[0033] where t1' is the minimum reaction time for calculating the maximum permissible speed and the minimum permissible distance; in the calculations, t1' = 0.3 s is assumed [Suvorov Yu. B. Application of differentiated values ​​of driver reaction time in expert practice: methodological recommendations / Yu. B. Suvorov; All-Union Scientific Research Institute of Forensic Expertise. - Moscow: VNIISE, 1987. - 21 p.];

[0034] t2– brake drive delay time, s;

[0035] t3– deceleration increase time, s.

[0036] The brake drive lag time t2 and the deceleration increase time t3 are variables that depend on the design features of the braking system, the mass of the vehicle and the adhesion of the tires to the road. For their numerical determination, the average values ​​​​given in [Grigoryan, V. G. Application of braking parameters of motor vehicles in expert practice: Methodological recommendations for experts / V. G. Grigoryan, A. V. Grigoryan. - Moscow: Russian Federal Center for Forensic Expertise under the Ministry of Justice of the Russian Federation, 2021. - 40 p. - ISBN 978-5-91133-224-2. - DOI 10.30764 / 978-5-91133-224-2-2021-3. - EDN DAPGEL.], which provides the values ​​of t2 and t3 for various vehicle categories (passenger cars, trucks, buses), load ranges (readings from sensor 8, which determines the vehicle's load level), and adhesion coefficients φ (readings from sensor 9, which determines the type and condition of the surface, based on a neural network model). Specific values ​​are selected by computing unit 6 based on their closest correspondence to current conditions.

[0037] The proposed road accident prevention system improves road safety by equipping the claimed system with a computing unit whose inputs are connected to the outputs of a driver's optical visibility sensor, a sensor that determines the vehicle load level, and a sensor that determines the type and condition of the road surface based on a neural network model. The output of the computing unit is connected to the controller input. This reduces the number of accidents and the severity of their consequences in conditions of poor visibility (rain, fog, snow), as well as the type and condition of the road surface, by forcibly limiting the speed to a safe speed in the event of a driver violating the safe speed limit.

Claims

A system for preventing road traffic accidents by equipping road signs and traffic lights requiring a reduction in speed or a stop of vehicles with devices that transmit coded information regarding the established traffic restrictions in the direction of their coverage area; each vehicle is equipped with a receiver of said information with a decoder and a controller that acts on the controls, characterized in that the system is also equipped with a computing unit, the inputs of which are connected to the outputs of an optical visibility sensor from the driver's seat, a sensor that determines the vehicle load level, and a sensor that determines the type and condition of the road surface based on a neural network model, and the output of the computing unit is connected to the input of the controller.

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