Abstract: A system for producing a trajectory from a diverse set of possible movements can include a processor and a memory. The memory can store a possible movement production module and a trajectory production module. The possible movement production module can include instructions that cause the processor to: (1) produce a set of possible movements of a mobile robot with respect to a challenge, (2) determine a measure of diversity between a first possible movement and a second possible movement, and (3) produce a subset. Measures of diversity associated with possible movements included in the subset can be larger than measures of diversity associated with possible movements excluded from the subset. The trajectory production module can include instructions that cause the processor to: (1) produce trajectories associated with the possible movements included in the subset and (2) cause the mobile robot to move according to one of the trajectories.

Abstract: A system and related method determines implicit lane boundaries by generating a bird's eye view of a portion of a road having a first lane and a second lane from environment data having information related to the road, overlaying a grid having cells onto the bird's eye view of the portion of the road, determining cells of the grid that form at least portions of the first lane of the road, determining cells of the grid that form at least portions of the second lane of the road, and determining a probability for one or more cells of the grid indicating a likelihood that a vehicle will travel upon portions of the road represented by the cells of the grid when traveling from the first lane to the second lane. The probability for one or more cells of the grid may be generated by a neural network trained with training data.

Abstract: A system and related method determines implicit lane boundaries by generating a bird's eye view of a portion of a road having a first lane and a second lane from environment data having information related to the road, overlaying a grid having cells onto the bird's eye view of the portion of the road, determining cells of the grid that form at least portions of the first lane of the road, determining cells of the grid that form at least portions of the second lane of the road, and determining a probability for one or more cells of the grid indicating a likelihood that a vehicle will travel upon portions of the road represented by the cells of the grid when traveling from the first lane to the second lane. The probability for one or more cells of the grid may be generated by a neural network trained with training data.