  Collision avoidance systems play a vital role in reducing the number of
vehicle accidents and saving human lives. This paper extends the previous work
using evolutionary neural networks for reactive collision avoidance. We are
proposing a new method we have called symmetric neural networks. The method
improves the model's performance by enforcing constraints between the network
weights which reduces the model optimization search space and hence, learns
more accurate control of the vehicle steering for improved maneuvering. The
training and validation processes are carried out using a simulation
environment - the codebase is publicly available. Extensive experiments are
conducted to analyze the proposed method and evaluate its performance. The
method is tested in several simulated driving scenarios. In addition, we have
analyzed the effect of the rangefinder sensor resolution and noise on the
overall goal of reactive collision avoidance. Finally, we have tested the
generalization of the proposed method. The results are encouraging; the
proposed method has improved the model's learning curve for training scenarios
and generalization to the new test scenarios. Using constrained weights has
significantly improved the number of generations required for the Genetic
Algorithm optimization.
