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DE-102024138367-A1 - SYSTEM AND METHOD FOR SETTING A VEHICLE'S SPEED LIMIT BASED ON ENVIRONMENTAL CONDITIONS

DE102024138367A1DE 102024138367 A1DE102024138367 A1DE 102024138367A1DE-102024138367-A1

Abstract

The present disclosure provides a system 102 and a method 300 for setting a vehicle's speed limit based on environmental conditions. The method 300 comprises detecting parameters 302 and a friction level along a route. Furthermore, the method 300 comprises predicting a total rainfall amount 304 based on the parameters and the total rainfall amount. The method 300 also includes determining a confidence level 306 for the prediction. Finally, the method 300 comprises setting a vehicle speed limit 308 based on the prediction. The system 102 and the method 300 therefore overcome the limitations of existing systems that produce false positive results by comparing the predicted values with individual sensor inputs, normalizing the error values, and dynamically adjusting the speed in real time based on environmental conditions. By predicting confidence levels from environmental data, this disclosure provides accurate speed settings to improve safety and efficiency compared to static or manual methods.

Inventors

  • Sourabha Koteshwara
  • Rishi Rangarajan
  • Anand Kop

Assignees

  • Mercedes-Benz Group AG

Dates

Publication Date
20260513
Application Date
20241217
Priority Date
20241111

Claims (10)

  1. Method (300) for setting a vehicle speed limit based on environmental conditions, comprising: Detecting (302), by one or more processors (104) associated with a system (102), one or more parameters associated with an environment and a friction level along a route; Predicting (304) a total rainfall level by the one or more processors (104) based on the one or more parameters and the friction level; Determining (306) a confidence level for the prediction by the one or more processors (104); and Setting (308) the vehicle speed limit by the one or more processors (104) based on the determination of the confidence level.
  2. Procedure (300) according to Claim 1 , wherein one or more parameters are received from one or more sensors associated with the system (102), and wherein one or more parameters include at least one of the following: a precipitation level and a wetness level along the route.
  3. Procedure (300) according to Claim 1 , wherein the prediction (304) of the total rainfall by the one or more processors (104) comprises: generating, by the one or more processors (104), a weighted value corresponding to each of the one or more parameters received from each of the one or more sensors associated with the system (102); determining, by the one or more processors (104), a weighted average value based on the weighted value; and determining the total rainfall by the one or more processors (104) based on the weighted average value.
  4. Procedure (300) according to Claim 3 , wherein the determination (306) of the confidence level by the one or more processors (104) comprises: prediction, by the one or more processors (104), of an individual rainfall level corresponding to each of the one or more parameters, based on the weighting value; comparison of the total rainfall and the individual rainfall corresponding to each of the one or more parameters, by the one or more processors (104); determination, by the one or more processors (104), of an error value between the total rainfall level and the individual rainfall level based on the comparison; and normalization of the error value by the one or more processors (104) to determine the confidence value.
  5. Procedure (300) according to Claim 4 , comprising: estimating an aquaplaning speed and a critical speed by the one or more processors (104) based on the determination of the total rainfall amount; and estimating the speed limit by the one or more processors (104) based on the aquaplaning speed, the critical speed and the confidence level.
  6. Procedure (300) according to Claim 5 , wherein the estimation of the critical speed by the one or more processors (104) comprises: determining a curvature profile of the track by the one or more processors (104); and determining the critical speed by the one or more processors (104) based on the curvature profile of the track.
  7. Procedure (300) according to Claim 5 , wherein the setting (308) of the speed limit by the one or more processors (104) comprises: determining by the one or more processors (104) that the confidence level is above a predefined threshold, based on the estimated speed limit; and setting the speed limit by the one or more processors (104) in response to the determination that the confidence level is greater than the predefined threshold.
  8. Procedure (300) according to Claim 1 , comprising: detecting the friction level between each wheel assigned to the vehicle and a surface of the track by one or more processors (104).
  9. System (102) for setting a vehicle speed limit based on environmental conditions, comprising: one or more processors (104); and a memory (106) operationally coupled to the one or more processors (104), the memory (106) comprising one or more instructions which, when executed, cause the one or more processors (104) to: detect one or more parameters associated with an environment and a degree of friction along a route; predict a total amount of rainfall based on the one or more parameters and the amount of friction; determine a confidence level for the prediction; and set the maximum speed of the vehicle based on the determination of the confidence level.
  10. System (102) according to Claim 9 , wherein, to predict the total rainfall, the one or more processors (104) are configured to: generate a weighting value corresponding to each of the one or more parameters received from each of the one or more sensors; determine a weighted average value based on the weighting value; and determine the total rainfall based on the weighted average value.

Description

TECHNICAL AREA The present disclosure relates to vehicle safety systems and in particular to a system and a method for setting a speed limit of a vehicle based on environmental conditions, such as road wetness and precipitation, in order to improve vehicle safety. BACKGROUND Vehicle safety systems have become increasingly sophisticated in recent years, incorporating various sensors and control units to improve the safety of drivers and passengers. A particular concern is the impact of adverse weather conditions, especially rain, on vehicle performance and safety. Rainfall can affect road conditions and reduce traction between tires and the road surface. This reduction in traction increases the risk of aquaplaning, where a layer of water forms between the tires and the road, leading to a loss of steering control and braking effectiveness. The severity of this risk depends on factors such as rainfall intensity, road surface condition, vehicle speed, and tire condition. Existing solutions primarily rely on optical sensors and windshield wiper status to detect rain. These methods are prone to false alarms, which can trigger unnecessary speed limits. Some advanced systems incorporate cameras and additional sensors such as humidity, temperature, and pressure sensors. However, camera-based methods can become less effective when the field of view is obstructed or computationally intensive. A critical shortcoming of existing solutions is their inability to account for scenarios where it is not currently raining, but the road is wet and slippery due to previous rainfall. Conversely, existing systems cannot distinguish between situations where it is raining slightly, but there is only a negligible difference in friction and tire grip on the road. These differentiated scenarios are not adequately considered by current technologies, leading to potential safety risks. Furthermore, the speed limits considered in existing solutions are often fixed and do not account for constantly changing weather conditions and their impact on wetness and road friction. Existing systems typically consider either the aquaplaning speed or the critical speed limit, but not both, meaning that important safety thresholds may be overlooked. Another significant limitation of existing solutions is the lack of robust mechanisms for validating the data in these systems. Without effective means of preventing false sensor alarms, these existing systems can trigger sudden speed limits in situations where they are not needed, potentially endangering surrounding vehicles. Many techniques have been developed to solve the problems mentioned above. The patent document describes this. US11535258B2 A method for determining and publishing a rain warning based on the evaluation of sensor data and the calculation of a confidence level for the weather conditions in a specified region. Another patent document CN112884288B Describes a system for evaluating driving safety in rain and fog on highways, comprising a module for estimating the road friction coefficient, a speed limiting model, a simulation platform, and a safety level assessment module to determine road friction, limit speed, and classify safety levels based on evaluation parameters. Another patent document CN114360270B describes a method and a system for determining the permissible maximum speed on motorways in adverse weather conditions using traffic meteorological data to analyze the visibility distance when parking, the visibility distance when making decisions, the sliding speed and the driving stability, thereby improving road safety and applicability. Conventional methods and systems lack a comprehensive approach for accurately predicting speed limits based on real-time environmental data and the dynamic response of multiple sensors. Furthermore, conventional methods and systems do not take into account... They do not consider factors such as road friction or aquaplaning risk and do not offer real-time speed adjustments based on immediate changes in precipitation intensity or vehicle stability. Furthermore, conventional methods and systems lack the integration of multiple sensor inputs to provide precise, context-aware speed recommendations. Therefore, conventional methods are limited to static evaluations and cannot effectively respond to rapid changes in road and weather conditions, which can compromise safety and efficiency. Therefore, there is a need for a more reliable and adaptable system that eliminates at least the aforementioned disadvantages and all other shortcomings, or at least provides a valuable alternative to existing procedures and systems. SUMMARY A general objective of the present disclosure is to provide a system and a method for dynamically adjusting a vehicle's speed limit based on real-time detection of environmental factors, such as rain and road wetness, thereby ensuring improved vehicle safety. One objective of the present disclosure is to eliminate discrepancies between