Further, the estimates from the dynamic grid can be predicted for a short-time in the future to assess the occupancy of the local environment in the near future. In addition to estimating the probability of occupancy, the dynamic occupancy grid also estimates the kinematic attributes of each cell, such as velocity, turn-rate, and acceleration. A dynamic occupancy grid map is a grid-based estimate of the local environment around the ego vehicle. For an example using the discrete set of objects, refer to the Highway Trajectory Planning Using Frenet Reference Path (Navigation Toolbox) example. In this example, you represent the surrounding environment as a dynamic occupancy grid map. A hybrid of these two approaches is also possible by extracting object hypothesis from the grid-based representation. The grid-based representation is also less sensitive to imperfections of object extraction such as false and missed targets. On the other hand, a grid-based approach allows for an object-model-free representation, which assists in efficient collision-checking in complex scenarios with large number of objects. It also allows for an easier way to define inter-object relations for behavior prediction.
For planning algorithms, the object-based representation offers a memory-efficient description of the environment. The choice of environment representation is typically governed by the upstream perception algorithm. In the presence of dynamic obstacles in the environment, a local motion planner requires short-term predictions of the information about the surroundings to assess the validity of the planned trajectories. Discrete set of objects in the surrounding environment with defined geometries.ĭiscretized grid with estimate about free and occupied regions in the surrounding environment.
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