Providing driving condition alerts using road attribute data
A method and system for providing a driving condition alert to vehicle drivers and others are disclosed. The alert is provided based on road location and attribute data; real-time, historic, and forecast traffic data; real-time, historic, and forecast weather data; and/or scheduled and unscheduled event data. The alert may include information indicating the reason for the alert provided. In addition, the alert may include a relative scale of hazard (e.g., a scale of 1-10 with 1 representing no hazard and 10 representing the highest hazardous condition). As a result of receiving the alert, the driver may adjust their route or driving behavior.
1. An apparatus comprising at least one processor and at least one memory storing computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
obtain road attribute data for a location, wherein the road attribute data indicates properties of a road segment at the location, wherein the road attribute data includes a weight associated with the road attribute data for the location;
obtain real-time weather information for the location, wherein the real-time weather information for the location is associated with a weight based, at least in part, on a type of weather at the location;
analyze the real-time weather information and the road attribute data based on a set of rules using the weight of the road attribute data for the location and a weight associated with the real-time weather information based on the type of weather at the location to determine whether a weighted combination of the real-time weather information and the road attribute data constitute a hazardous driving condition at the location;
if the hazardous driving condition exists, generate a hazardous driving condition message comprising data identifying the location and data identifying the hazardous driving condition; and
transmit over a communications network the hazardous driving condition message to a navigation device.
2. The apparatus of claim 1 wherein the real-time weather information is a temperature at the location, and the hazardous driving condition is icy pavement on the bridge.
3. The apparatus of claim 1 wherein the real-time weather information is a wind speed at the location.
4. The apparatus of claim 1 wherein the hazardous driving condition message comprises data indicating a hazardous driving condition description.
5. The apparatus of claim 1 wherein the hazardous driving condition message comprises data indicating a hazardous driving condition confidence level.
6. The apparatus of claim 1 wherein the hazardous driving condition message comprises data indicating a level of risk above normal driving conditions.
7. The apparatus of claim 1 wherein a machine learning algorithm analyzes the real-time weather information and the road attribute information to determine whether the hazardous driving condition exists.
8. The apparatus of claim 7 , wherein the machine learning algorithm is a neural network.
9. A method comprising:
obtaining, by a processor, road attribute data for a road segment, wherein the road attribute data includes a weight associated with the road attribute data for the road segment;
obtaining weather information and traffic information for the road segment, wherein the weather information for the road segment is associated with a weight based, at least in part, on a type of weather along the road segment;
analyzing the weighted weather information, the traffic information, and the weighted road attribute data based on a set of rules to determine whether a combination of the weighted weather information, the traffic information, and the weighted road attribute data constitute a hazardous driving condition on the road segment;
if the hazardous driving condition exists, generating a hazardous driving condition message comprising data identifying the road segment, data indicating a hazardous driving condition description, and data indicating a hazardous driving condition confidence level; and
transmitting over a communications network the hazardous driving condition message to a navigation device.
10. The method of claim 9 wherein the hazardous driving condition message comprises data indicating a level of risk above normal driving conditions.
11. The method of claim 9 wherein the weather information is real-time weather information or forecast weather information.
12. The method of claim 9 wherein the weather information indicates at least one of precipitation, temperature, visibility, wind, humidity and barometric pressure.
13. The method of claim 9 wherein the traffic information is at least one of real-time traffic information or forecast traffic information and historic traffic information.
14. The method of claim 9 wherein the traffic information is at least one of traffic accident, congestion, construction, lane closure, traffic speed and traffic volume.
15. The method of claim 9 wherein the road attribute data is at least one of curvature, slope, elevation, grade, bank, width, speed limit, road regulations, tunnel, bridge, payment type, number of lanes, lane width, lane markings, lane changes, and lane medians.
16. The method of claim 9 wherein the road attribute data is a content of a warning sign associated with the road segment.
17. The method of claim 9 , wherein a machine learning algorithm analyzes the weather information, the traffic information and the road attribute data to determine whether the hazardous driving condition exists.
18. The method of claim 16 , wherein the machine learning algorithm is a neural network.
19. A non-transitory computer-readable storage medium for determining whether to generate a hazardous condition warning, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:
obtaining, by a processor, road attribute data for a road segment, wherein the road attribute data includes a weight associated with the road attribute data for the road segment;
obtaining weather information and traffic information for the road segment, wherein the weather information for the road segment is associated with a weight based, at least in part, on a type of weather along the road segment;
analyzing the weighted weather information, the traffic information and the weighted road attribute data based on a set of rules to determine a risk of a hazardous driving condition on the road segment based on a combination of the weighted weather information, the traffic information, and the weighted road attribute data;
if the risk of the hazardous driving condition is greater than a threshold, generating a hazardous driving condition message comprising data identifying the road segment and data indicating a hazardous driving condition description; and
transmitting over a communications network the hazardous driving condition message to a navigation device.
20. The non-transitory computer-readable storage medium of claim 19 , wherein a machine learning algorithm analyzes the weather information, the traffic information and the road attribute data to determine whether the hazardous driving condition exists.