Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Tracking and mapping the local environment form the basis of an autonomous vehicle system. They are often realized separately using occupancy grids, which do not require object or shape assumptions, and model-based object tracking algorithms. Many approaches require a binary classification of the sensor measurements into coming from a static or from a dynamic object, as otherwise inconsistencies beten the different representations are likely to occur.  presents grid-based tracking and mapping (GTAM), a low-level grid-based approach that simultaneously estimates the static and the dynamic environment, their uncertainties, velocities, as ll as information about free space. GTAM works on the level of grid cells, rather than creating object hypotheses. A particle filter is used to obtain continuous cell velocity distributions for all obstacles. Continuous evidences in a Dempster–Shafer model are derived without requiring a binary pre-classification of the sensor measurements. Results and evaluations using a vehicle moving in real dynamic street environments demonstrate the performance of the presented approach.
['Georg Tanzmeister', 'Dirk Wollherr']
I am writing a research paper on a idea Evidential Grid-Based Tracking and Mapping
[{"from": "human", "value": "I am writing a research paper on a idea Evidential Grid-Based Tracking and Mapping"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Tracking and mapping the local environment form the basis of an autonomous vehicle system. They are often realized separately using occupancy grids, which do not require object or shape assumptions, and model-based object tracking algorithms. Many approaches require a binary classification of the sensor measurements into coming from a static or from a dynamic object, as otherwise inconsistencies beten the different representations are likely to occur.  presents grid-based tracking and mapping (GTAM), a low-level grid-based approach that simultaneously estimates the static and the dynamic environment, their uncertainties, velocities, as ll as information about free space. GTAM works on the level of grid cells, rather than creating object hypotheses. A particle filter is used to obtain continuous cell velocity distributions for all obstacles. Continuous evidences in a Dempster\u2013Shafer model are derived without requiring a binary pre-classification of the sensor measurements. Results and evaluations using a vehicle moving in real dynamic street environments demonstrate the performance of the presented approach."}]