- The Road Condition Observer from Continental enables a classification of the road conditions with regard to tire/road friction.
- The system uses in a first application sensors that are installed in the vehicle as standard to classify the road surface as dry, wet, covered with snow or icy.
- Advanced driver assistance systems such as the triggering time of emergency brake assists can be adapted to the possibly longer braking distance due to bad weather conditions.
Wet and icy road conditions are frequent causes of accidents. Even experienced drivers can misjudge how well the tires are gripping the road. Continental is developing a new solution called Road Condition Observer in response to this threat. A specially developed algorithm detect the typical features for the four different road conditions: dry, wet, snow-covered and icy. Currently, detecting wet conditions is undergoing advanced testing at vehicle manufacturers.
“We use sensors available in the vehicle for the Road Condition Observer to gain information on the grip of the road surface,” says Bernd Hartmann, Head of the Enhanced ADAS (Advanced Driver Assistance Systems) & Tire Interactions project group within the Advanced Engineering department of Continental’s Chassis & Safety division. “This knowledge allows us to adjust the functions of advanced driver assistance systems to the actual road conditions. To prevent an impending collision, automatic emergency braking for example must be initiated considerably earlier on a wet road than on a dry one.”
Currently, it is solely the task of drivers to assess the weather conditions and observations and to combine it with his observations of the vehicle surroundings to draw the correct conclusions on the alleged condition of the road. In the future the Road Condition Observer will support the driver and will make the Advanced Driver Assistance Systems capable of detecting adverse conditions and to react suitably and in good time. Hartmann believes that this capability will become more important in the future: “Automated driving also requires us to make judging the condition of the road technically possible for the system. An automated vehicle in particular must know if it is icy so that it can drive safely around the next corner.”
Knowledge of the Traction between Tires and Road Influences Active Driving Safety
The automotive industry has been researching for years into methods for detecting the available friction coefficient as an interaction between tires and the road surface in a proactive way and to use this information to make their vehicles safer. This variable measures the force that a tire can apply to the road and is also known more simply as grip. All earlier attempts in this field failed due to inadequate sensors and computing capacity that was either lacking or too expensive. After intensive development work, Continental has managed to develop a system that recognizes the road condition and allows a classification in dry, wet, snow-covered and icy. In addition to the vehicle dynamics sensors in the car, a mono camera is also used. The advantage of this is: Electronic Stability Control (ESC) is available in nearly every car as standard equipment and mono cameras are available in an increasing number of vehicles due to the growing distribution of driver assistance Systems.
The Road Condition Observer classifies the road condition on the basis of the evaluation of camera images of the surroundings in front of the car and comparison with vehicle dynamics data from the ESC, knowledge of local and regional weather data (temperature, wiper activity and cloud data) as well as the tire behavior. In a subsequent step, a friction coefficient can be derived from this. This means that the Road Condition Observer contributes to increase active driving safety.
In the course of further development, the information of the Road Condition Observer will flow into a comprehensive 360° environment model that is prerequisite for a comprehensive understanding of the overall driving scenario. The environment model is generated through the fusion of different information sources. The integration of the road geometry, localization and traffic regulation recognition, model-based tracking of moving objects as well as the identification of free space play an important role.