Huang, Allen and Nitschke, Geoff (2017) Evolving Collective Driving Behaviors, Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 1573-1574, ACM Press.
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Abstract
Recently there has been increased research interest in developing autonomous, adaptive control systems of self-driving vehicles. However, there has been little work on synthesizing collective behaviours for autonomous vehicles that must safely interact and coordinate so as traffic throughput on any given road network is maximized. This work uses neuro-evolution to automate car controller design, testing various vehicle sensor configurations and collective driving behaviours resulting from car interactions on roads without constraints of traffic lights, stop signals at intersections or lanes that vehicles must adhere to and thus simulates potential future scenarios where vehicles must drive autonomously without special road infrastructure constraints. Results indicate that neuro-evolution is an effective method for automatically synthesizing collective driving behaviours that are behaviourally robust across a range of vehicle sensor configurations and generalize to different task environments.
| Item Type: | Conference poster |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence |
| Date Deposited: | 23 Nov 2017 |
| Last Modified: | 10 Oct 2019 15:31 |
| URI: | http://pubs.cs.uct.ac.za/id/eprint/1186 |
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