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In autonomous driving applications a critical challenge is to identify actionto take to avoid an obstacle on collision course. For example, when a heavyobject is suddenly encountered it is critical to stop the vehicle or change thelane even if it causes other traffic disruptions. However,there are situationswhen it is preferable to collide with the object rather than take an actionthat would result in a much more serious accident than collision with theobject. For example, a heavy object which falls from a truck should be avoidedwhereas a bouncing ball or a soft target such as a foam box need not be.Wepresent a novel method to discriminate between the motion characteristics ofthese types of objects based on their physical properties such as bounciness,elasticity, etc.In this preliminary work, we use recurrent neural net-work withLSTM cells to train a classifier to classify objects based on their motiontrajectories. We test the algorithm on synthetic data, and, as a proof ofconcept, demonstrate its effectiveness on a limited set of real-world data.

Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network