Vehicle Sliding Detection Using Actual vs Expected Driving Behavior
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Solution Overview
Problem
Vehicles sliding on slippery roads pose a safety risk due to inadequate control by drivers, which can lead to accidents and endanger other road users.
Innovation Solution
A method to determine if a second vehicle is sliding by comparing actual and expected driving behavior data, using sensors and data processing to identify slippage beyond a predefined threshold, and implement collision mitigation maneuvers or warnings for other vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sliding vehicle is detected by comparing actual and expected driving behavior data, then road safety is improved and collision risks are reduced, but the device complexity and data processing requirements increase
Solution Approach 1:
The detection system is segmented into multiple independent components: obtaining actual driving behavior data from sensors, obtaining expected driving behavior data from maps and vehicle parameters, comparing the two data sets, and executing mitigation maneuvers. This modular segmentation allows each component to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
Expected driving behavior data is pre-calculated and stored based on road geometry, traffic rules, and typical driving patterns before actual detection is needed. This preliminary action enables rapid comparison with actual behavior during critical moments, improving detection speed and reliability without adding real-time processing complexity.
2Reliability
If real-time comparison of actual and expected driving behavior is performed to detect sliding, then detection reliability is improved, but the processing time and computational resources increase
Solution Approach 1:
Expected driving behavior data including trajectory, speed profiles, and steering angles are pre-calculated offline based on road maps, traffic rules, and vehicle characteristics. This pre-computation stores reference data in accessible formats, enabling rapid real-time comparison with actual sensor data without heavy computational burden during critical detection moments.
Solution Approach 2:
The comparison process focuses on locally relevant parameters at the vehicle's current position and immediate future trajectory, rather than analyzing entire historical data sets. This local quality approach processes only the most critical segments of driving behavior data, reducing computational time while maintaining detection accuracy for sliding conditions.
Data Source
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AI summary
The disclosure relates to a method for determining whether a second vehicle (14) is sliding from a perspective of a first vehicle (10) or from a perspective of an infrastructure element. The method comprises obtaining first data indicative of an actual driving behavior of the second vehicle (14), obtaining second data indicative of an expected driving behavior of the second vehicle (14), and determining whether the second vehicle (14) is sliding or not based on a comparison of the first data and the second data. Further, the disclosure relates to a method for controlling the first vehicle (10). Moreover, the disclosure is directed to a data processing apparatus comprising means for carrying out any of said methods as well as a computer program and a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out any of said methods.