One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this paper, we propose a hierarchical driving model with explicit model of continuous intention and continuous dynamics, which decouples the complexity in the observation-to-action reasoning in the human driving data. Specifically, the continuous intention module takes the route planning and perception to generate a potential map encoded with obstacles and goals being expressed as grid based potentials. Then, the potential map is regarded as a condition, together with the current dynamics, to generate the trajectory . The trajectory is modeled by a network based continuous function approximator , which naturally reserves the derivatives for high-order supervision without any additional parameters. Finally, we validate our method on both datasets and simulators, demonstrating superior performance . The method is also deployed on the real vehicle with loop latency, validating its effectiveness  .
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this paper, we propose a hierarchical driving model with explicit model of continuous intention and continuous dynamics, which decouples the complexity in the observation-to-action reasoning in the human driving data. Specifically, the continuous intention module takes the route planning map obtained by GPS and IMU, perception from a RGB camera and LiDAR as input to generate a potential map encoded with obstacles and intentions being expressed as grid based potentials. Then, the potential map is regarded as a condition, together with the current dynamics, to generate a continuous trajectory as output by a continuous function approximator network, whose derivatives can be used for supervision without additional parameters. Finally, we validate our method on both datasets and simulator, demonstrating that our method has higher prediction accuracy of displacement and velocity and generates smoother trajectories . The method is also deployed on the real vehicle with loop latency, validating its effectiveness . To the best of our knowledge, this is the first work to produce the driving trajectory using a continuous function approximator network .
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and perception
map obtained by GPS and IMU, perception from a RGB camera and LiDAR as input
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goals
intentions
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the trajectory . The trajectory is modeled by a network based
a continuous trajectory as output by a
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, which naturally reserves the derivatives for high-order supervision without any
network, whose derivatives can be used for supervision without
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simulators, demonstrating superior performance
simulator, demonstrating that our method has higher prediction accuracy of displacement and velocity and generates smoother trajectories
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. To the best of our knowledge, this is the first work to produce the driving trajectory using a continuous function approximator network