Ev Evolutionary Automation of Coordinated Aut Autono nomous us - - PowerPoint PPT Presentation
Ev Evolutionary Automation of Coordinated Aut Autono nomous us Vehi hicles Chien-Lun Huang (Allen) and Dr Geoff Nitsschke Increased research in adaptive control systems for autonomous vehicles. Existing research focuses on autonomous
systems for autonomous vehicles.
control of single vehicle or limited noise in the environment
various evolutionary search methods for autonomous vehicle behaviour
Neuro-Evolution of Augmenting Topologies (NEAT) directed evolutionary search. We compared three different search methods: 1. Objective-based 2. Behaviour-based (Novelty Search) 3. Hybrid (Objective + Behaviour) Goal: evolve effective and efficient coordinated driving behaviour through given roads
Neuro-Evolution of Augmenting Topologies (NEAT) directed evolutionary search. We compared three different search methods: 1. Objective-based
Neuro-Evolution of Augmenting Topologies (NEAT) directed evolutionary search. We compared three different search methods: 1. Objective-based 2. Behaviour-based (Novelty Search)
k – k-Nearest Neighbours in generation + archive dist – Euclidean Distance μ – ith nearest neighbour X – behaviour w.r.t novelty metric Behaviour Characterisation: Speed and Cohesion, Speed* and Location
*experimentally determined
Neuro-Evolution of Augmenting Topologies (NEAT) directed evolutionary search. We compared three different search methods: 1. Objective-based 2. Behaviour-based (Novelty Search) 3. Hybrid (Objective + Behaviour)
ρ = 0.5, equally combining fitness and novelty Fitness and Novelty score normalised before combination
which performs the best in evolving vehicle driving behaviour.
tested with various different configurations (including unseen configurations [vehicle pool sizes] and environments [tracks])
controllers.
experiments – each with static obstacles and varying altitude
Evolution Results: Hybrid > Objective (Fitness) and Hybrid All Methods achieved > 60% task performance. Supports existing research that hybrid approach can improve task performance in this type of task
Search space exploration heatmaps for each method indicates:
and 0.4 task performance for all generations
novelty search since it’s a behavior maintenance technique, but low task performance overall. 80% within 0.0 and 0.3 task performance.
both O and N, was able to achieve higher task performance due to this.
Objective-based (fitness) Behaviour-based (novelty search) Hybrid
Evaluation Results: Objective (Fitness)-evolved controllers generalized the best Hybrid-evolved controllers generalized the worst
Visualization of fittest controllers by each method (O, N, H) helps explain hybrid’s high task-performance on evolution track but inability to generalize as well as O and N. Both O and N evolved higher neural complex controllers whereas Hybrid evolved reactive networks that performed well on the evolution track only.