Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Before taking a decision, a driver anticipates the future behavior of other traffic participants. Hover, if a driver is inattentive or overloaded, he may fail to consider relevant information. This can lead to bad decisions and potentially result in an accident. A computational system that is designed to anticipate other traffic participants' behaviors could assist the driver in his decision making by sending him an early warning when a risk of collision is predicted. Existing research in this area usually focuses on only one of two aspects, i.e., quality or scope. Quality refers to the ability to warn a driver early before a dangerous situation happens. Scope is the diversity of scenarios in which the approach can work. In general,  see methods targeting a broad scope but showing low quality, with others having a narrow scope but high quality. Our goal is to create a system with high quality and high scope. To achieve this,  propose an architecture that combines classifiers to predict behaviors for many scenarios. ,  will first introduce the generic concept of such a system applicable to highway and inner-city scenarios.  will show that a combination of general and specific classifiers is a solution to improve quality and scope based on a concrete implementation for lane-change prediction in highway scenarios. 2014 IEEE. Language: en
['Sarah Bonnin', 'Thomas H. Weisswange', 'Franz Kummert', 'Jens Schmuedderich']
I am writing a research paper on a idea General behavior prediction by a combination of scenario-specific models
[{"from": "human", "value": "I am writing a research paper on a idea General behavior prediction by a combination of scenario-specific models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Before taking a decision, a driver anticipates the future behavior of other traffic participants. Hover, if a driver is inattentive or overloaded, he may fail to consider relevant information. This can lead to bad decisions and potentially result in an accident. A computational system that is designed to anticipate other traffic participants' behaviors could assist the driver in his decision making by sending him an early warning when a risk of collision is predicted. Existing research in this area usually focuses on only one of two aspects, i.e., quality or scope. Quality refers to the ability to warn a driver early before a dangerous situation happens. Scope is the diversity of scenarios in which the approach can work. In general,  see methods targeting a broad scope but showing low quality, with others having a narrow scope but high quality. Our goal is to create a system with high quality and high scope. To achieve this,  propose an architecture that combines classifiers to predict behaviors for many scenarios. ,  will first introduce the generic concept of such a system applicable to highway and inner-city scenarios.  will show that a combination of general and specific classifiers is a solution to improve quality and scope based on a concrete implementation for lane-change prediction in highway scenarios. 2014 IEEE. Language: en"}]