  Collision avoidance systems can play a vital role in reducing the number of
accidents and saving human lives. In this paper, we introduce and validate a
novel method for vehicles reactive collision avoidance using evolutionary
neural networks (ENN). A single front-facing rangefinder sensor is the only
input required by our method. The training process and the proposed method
analysis and validation are carried out using simulation. Extensive experiments
are conducted to analyse the proposed method and evaluate its performance.
Firstly, we experiment the ability to learn collision avoidance in a static
free track. Secondly, we analyse the effect of the rangefinder sensor
resolution on the learning process. Thirdly, we experiment the ability of a
vehicle to individually and simultaneously learn collision avoidance. Finally,
we test the generality of the proposed method. We used a more realistic and
powerful simulation environment (CarMaker), a camera as an alternative input
sensor, and lane keeping as an extra feature to learn. The results are
encouraging; the proposed method successfully allows vehicles to learn
collision avoidance in different scenarios that are unseen during training. It
also generalizes well if any of the input sensor, the simulator, or the task to
be learned is changed.
