Predictive Road Friction Control for Autonomous Driving Stability
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Solution Overview
Problem
Current computer-assisted or autonomous driving (CA/AD) vehicles face challenges in handling abrupt changes in road driving conditions due to their limited ability to collect and utilize data on road surface friction beyond their immediate location, leading to inefficient driving strategies and increased risk of losing control.
Innovation Solution
An external road surface condition data source collects and processes friction data from multiple vehicles over a road section, communicating this data to a CA/AD vehicle ahead, which uses a sensor interface, communication interface, and driving strategy unit to determine a proactive driving strategy based on the received data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ESP continuously adjusts braking to compensate for slippery road conditions, then the vehicle can maintain stability to a certain degree, but it cannot handle abrupt changes in road driving conditions, leading to reduced efficiency and increased risk
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing friction data from multiple sources (sensors, map data, weather information) in advance to predict upcoming road conditions. This allows the vehicle to proactively adjust driving parameters before encountering abrupt changes, rather than reacting passively after the fact.
Solution Approach 2:
The system dynamically adjusts driving parameters (speed, acceleration, steering) based on real-time friction predictions and actual sensor feedback. The driving strategy is continuously optimized by comparing predicted friction with actual friction measurements, allowing adaptive response to changing road conditions.
2Device complexity
If the vehicle uses only sensor data from the current location, then the system remains simple, but it is limited in handling weather-based driving condition changes and cannot anticipate future road conditions
Solution Approach 1:
The system merges multiple data sources including on-vehicle sensors, pre-stored map friction data, and real-time weather information to create a comprehensive friction prediction model. This combination allows the system to anticipate road conditions ahead without requiring overly complex hardware.
Solution Approach 2:
The system uses an intermediary friction prediction model that processes and integrates data from multiple sources (sensors, maps, weather) to predict upcoming road conditions. This intermediary layer translates raw data from various sources into actionable friction predictions without requiring direct complex connections between all data sources.
3Reliability
If the vehicle slows down rapidly or stops to handle abrupt road condition changes, then safety is maintained, but driving efficiency is reduced
Solution Approach 1:
By predicting friction conditions ahead of time using multiple data sources, the system can gradually adjust driving parameters over a longer distance and time period, avoiding sudden stops or rapid deceleration while maintaining safety margins.
Solution Approach 2:
The system continuously adjusts driving parameters (speed, acceleration, steering angle) based on predicted friction values and actual sensor feedback, optimizing the balance between safety and efficiency by making smooth, progressive parameter changes rather than abrupt corrections.
Data Source
AI summary
Embodiments include apparatuses, methods, and systems for computer assisted or autonomous driving (CA/AD). An apparatus for CA/AD may include a sensor interface, a communication interface, and a driving strategy unit. The sensor interface may receive sensor data indicative of friction between a road surface of a current location of a CA/AD vehicle and one or more surfaces of one or more tires of the CA/AD vehicle. The communication interface may receive, from an external road surface condition data source, data indicative of friction for a surface of a road section ahead of the current location of the CA/AD vehicle. The driving strategy unit may determine, based at least in part on the sensor data and the data received from the external road surface condition data source, a driving strategy for the CA/AD vehicle beyond the current location of the CA/AD vehicle. Other embodiments may also be described and claimed.