GridSim: A Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving
This work addresses control methods for autonomous driving, but it is incremental as it compares existing deep learning approaches in a new simulator.
The authors tackled the problem of autonomous vehicle control by introducing GridSim, a simulator engine for generating occupancy grids, and compared deep reinforcement learning with genetic algorithms for driving behavior, achieving performance evaluated on highways, curved roads, and inner-city scenarios with specific fitness functions.
Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Another possible solution is deep reinforcement learning, but such a method requires that the agent interacts with its surroundings in a simulated environment. In this paper we introduce GridSim, which is an autonomous driving simulator engine running a car-like robot architecture to generate occupancy grids from simulated sensors. We use GridSim to study the performance of two deep learning approaches, deep reinforcement learning and driving behavioral learning through genetic algorithms. The deep network encodes the desired behavior in a two elements fitness function describing a maximum travel distance and a maximum forward speed, bounded to a specific interval. The algorithms are evaluated on simulated highways, curved roads and inner-city scenarios, all including different driving limitations.