Long term driving danger prediction system

1. A guidance system for a vehicle, comprising:

a processor executing a code for:

receiving an input from sensors, the input indicating surrounding traffic participants and information regarding a driving situation;

receiving a local map locating the vehicle;

creating a graph representation where traffic participants and static elements are the vertices and the edges are relations between pairs of vertices;

adding attributes to the vertices and edges of the graph based on information obtained on the driving vehicle, the traffic participants and additional information;

creating or obtaining a codebook of dangerous driving situations, each represented as graphs;

performing subgraph matching between the graphs in the codebook and the graph representing a current driving situation to select a set of matching graphs from the codebook;

calculating a distance between each selected codebook graph and the matching subgraph of the current driving situation;

determining potential dangers from codebook graphs with a low distance; and

generating an alert if one or more of the codebook dangers are imminent.

2. The system of claim 1 , comprising predicting a dangerous driving situation a few seconds before occurrence.

3. The system of claim 1 , comprising an input modality obtained from radar.

4. The system of claim 1 , comprising an input modality obtained from a video camera with a structure from motion 3D generator.

5. The system of claim 1 , comprising recognizing object types using computer vision on a video image as an input modality.

6. The system of claim 1 , comprising using GPS coordinates from a vehicle sensor combined with a GPS-enabled local map.

7. The system of claim 1 , comprising receiving as an input modality one or more of:

a driver status including sleepiness, aggressiveness and impairment;

a weather status including road surface, temperature, daylight, rain, snow;

a traffic situation proximal to the vehicle including roadwork, delays, accident; and

vehicle physical characteristics including length, width, weight, braking capabilities.

8. The system of claim 1 , wherein the codebook is populated by known dangerous driving situations.

9. The system of claim 1 , wherein the codebook is refined by using a database of recorded driving and unsupervised learning techniques to discover attribute ranges of traffic participant relations.

10. The system of claim 1 , wherein a distance function is defined between subgraphs, wherein said distance function between subgraphs is learned by supervised machine learning using subgraph attributes as features.

11. The system of claim 10 , wherein one classifier per each codebook graph is trained using labeled data to predict a distance between two matching subgraphs.

12. The system of claim 1 , wherein a distance function is defined between subgraphs, wherein said distance function between subgraphs is learned using supervised machine learning using statistics of the attributes and a single classifier is trained for the codebook.

13. The system of claim 1 , wherein a distance function is defined between subgraphs, wherein said distance function is crafted for each codebook graph based on a particular set of attributes.

14. The system of claim 1 , where a distance function is defined between subgraphs, wherein said distance function is a generic distance based on measured ranges of attributes.

15. The system of claim 1 , comprising sounding an audible alert, a 3D-localized audible alert to attract a driver's attention to a location of a potential danger, or a voice alert telling the driver about a danger.

16. The system of claim 1 , comprising displaying one or more of: a warning message on the dashboard, a warning message on the windshield, a warning hologram.

17. The system of claim 1 , comprising illuminating or projecting a highlight on the windshield, side windows or car interior to locate traffic participants involved in a dangerous situation.

18. The system of claim 1 , vibrating the steering wheel, brakes, accelerator or seat to warn of danger.

19. The system of claim 1 , comprising acting on the brakes or an ability of a driver to accelerate.

20. A method executed on a vehicle guidance system with sensors to capture surrounding traffic participants and information regarding a driving situation to assist a driver with a dangerous condition, comprising:

creating a graph representation where traffic participants and static elements are the vertices and the edges are relations between pairs of vertices;

adding attributes to the vertices and edges of the graph based on information obtained on the driving vehicle, the traffic participants and additional information;

creating or obtaining a codebook of dangerous driving situations, each represented as graphs;

performing subgraph matching between the graphs in the codebook and the graph representing a current driving situation to select a set of matching graphs from the codebook;

calculating a distance between each selected codebook graph and the matching subgraph of the current driving situation;

determining potential dangers from codebook graphs with a low distance; and;

generating an alert if one or more of the codebook dangers are imminent.