  Autonomous vehicles are controlled today either based on sequences of
decoupled perception-planning-action operations, either based on End2End or
Deep Reinforcement Learning (DRL) systems. Current deep learning solutions for
autonomous driving are subject to several limitations (e.g. they estimate
driving actions through a direct mapping of sensors to actuators, or require
complex reward shaping methods). Although the cost function used for training
can aggregate multiple weighted objectives, the gradient descent step is
computed by the backpropagation algorithm using a single-objective loss. To
address these issues, we introduce NeuroTrajectory, which is a multi-objective
neuroevolutionary approach to local state trajectory learning for autonomous
driving, where the desired state trajectory of the ego-vehicle is estimated
over a finite prediction horizon by a perception-planning deep neural network.
In comparison to DRL methods, which predict optimal actions for the upcoming
sampling time, we estimate a sequence of optimal states that can be used for
motion control. We propose an approach which uses genetic algorithms for
training a population of deep neural networks, where each network individual is
evaluated based on a multi-objective fitness vector, with the purpose of
establishing a so-called Pareto front of optimal deep neural networks. The
performance of an individual is given by a fitness vector composed of three
elements. Each element describes the vehicle's travel path, lateral velocity
and longitudinal speed, respectively. The same network structure can be trained
on synthetic, as well as on real-world data sequences. We have benchmarked our
system against a baseline Dynamic Window Approach (DWA), as well as against an
End2End supervised learning method.
