Using long-range dynamics and mental-state models to assess collision risk for early warning
View Patent ↗One embodiment of the present invention provides a system that for facilitating assessment of collision between a primary principal and a non-primary principal for early warning. During operation, the system periodically performs the following operations: The system obtains a current observation of the primary principal and non-primary principal. The system then assesses one or more future states for the primary and non-primary principals, respectively, based on: the current observation of the primary and non-primary principals, a dynamics model of the primary principal, and a mental-state model of a person associated with the primary principal. The system further produces one or more results which indicate an assessment of collision between the primary and non-primary principals.
1. A method for facilitating assessment of collision between a primary principal and a non-primary principal for early warning, the method comprising periodically performing:
obtaining a current observation of the primary principal and non-primary principal;
assessing one or more future states for the primary and non-primary principals, respectively, based on:
the current observation of the primary and non-primary principals,
a dynamics model of the primary principal, and
a mental-state model of a person associated with the primary principal; and
producing one or more results which indicate an assessment of collision between the primary and non-primary principals.
2. The method of claim 1 ,
wherein assessing the future states for the primary and non-primary principals comprises performing sequential Bayesian filtering based on the current observation and past observations of the primary and non-primary principals, respectively.
3. The method of claim 2 ,
wherein performing sequential Bayesian filtering comprises performing particle filtering.
4. The method of claim 2 ,
wherein performing sequential Bayesian filtering comprises performing Interacting Multiple Model (IMM) filtering.
5. The method of claim 1 ,
wherein the dynamics model of the primary principal describes the movements of the primary principal based on a scenario.
6. The method of claim 1 ,
wherein assessing the future states of the non-primary principal is based on a dynamics model which describes the movements of the non-primary principal based on a scenario.
7. The method of claim 1 ,
wherein the mental-state model includes an “alert” state and a “not-alert” state; and
wherein the mental-state model specifies a first probability of transition from the “alert” state to the “not-alert” state and a second probability of transition from the “not-alert” state to the “alert” state.
8. The method of claim 1 ,
wherein the mental-state model includes a “rational-decision” state and an “irrational-decision” state; and
wherein the mental-state model specifies a first probability of transition from the “rational-decision” state to the “irrational-decision” state and a second probability of transition from the “irrational-decision” state to the “rational-decision” state.
9. The method of claim 1 ,
wherein a state of the primary or non-primary principal includes one or more of:
a position;
a velocity; and
a mental state of the person associated with the primary or non-primary principal.
10. The method of claim 1 ,
where the results include one or more of:
a probability of collision,
a predicted time of collision,
a predicted location of collision,
a predicted benefit of collision warning, and
an estimated prediction accuracy.
11. A system for facilitating assessment of collision between a primary principal and a non-primary principal for early warning, the system comprising:
a specialized assessment mechanism, comprising:
a data obtaining mechanism configured to obtain a current observation of the primary principal and non-primary principal;
a computation mechanism configured to assess one or more future states for the primary and non-primary principals, respectively, based on:
the current observation of the primary and non-primary principals,
a dynamics model of the primary principal, and
a mental-state model of a person associated with the primary principal; and
a result producing mechanism configured to produce one or more results which indicate an assessment of collision between the primary and non-primary principals.
12. The system of claim 11 ,
wherein while assessing the future states for the primary and non-primary principals, the computation mechanism is configured to perform sequential Bayesian filtering based on the current observation and past observations of the primary and non-primary principals, respectively.
13. The system of claim 12 ,
wherein while performing sequential Bayesian filtering, the computation mechanism is configured to perform particle filtering.
14. The system of claim 12 ,
wherein while performing sequential Bayesian filtering, the computation mechanism is configured to perform Interacting Multiple Model (IMM) filtering.
15. The system of claim 11 ,
wherein the dynamics model of the primary principal describes the movements of the primary principal based on a scenario.
16. The system of claim 11 ,
wherein while assessing the future states of the non-primary principal, the computation mechanism is configured to apply a dynamics model which describes the movements of the non-primary principal based on a scenario.
17. The system of claim 11 ,
wherein the mental-state model includes an “alert” state and a “not-alert” state; and
wherein the mental-state model specifies a first probability of transition from the “alert” state to the “not-alert” state and a second probability of transition from the “not-alert” state to the “alert” state.
18. The system of claim 11 ,
wherein the mental-state model includes a “rational-decision” state and an “irrational-decision” state; and
wherein the mental-state model specifies a first probability of transition from the “rational-decision” state to the “irrational-decision” state and a second probability of transition from the “irrational-decision” state to the “rational-decision” state.
19. The system of claim 11 ,
wherein a state of the primary or non-primary principal includes one or more of:
a position;
a velocity; and
a mental state of the person associated with the primary or non-primary principal.
20. The system of claim 11 ,
where the results include one or more of:
a probability of collision,
a predicted time of collision,
a predicted location of collision,
a predicted benefit of collision warning, and
an estimated prediction accuracy.
21. A computer system for facilitating assessment of collision between a primary principal and a non-primary principal for early warning, the system comprising:
a processor;
a memory; and
a specialized assessment mechanism comprising:
a data obtaining mechanism configured to obtain a current observation of the primary principal and non-primary principal;
a computation mechanism configured to assess one or more future states for the primary and non-primary principals, respectively, based on:
the current observation of the primary and non-primary principals,
a dynamics model of the primary principal, and
a mental-state model of a person associated with the primary principal.
22. The computer system of claim 21 ,
wherein while assessing the future states for the primary and non-primary principals, the computation mechanism is configured to perform sequential Bayesian filtering based on the current observation and past observations of the primary and non-primary principals, respectively.