Evolutionary Hyperparameter Optimization to Find Lightweight CNN Models for Autonomous Steering
This research investigates the optimization of Convolutional and Dense Neural Networks (CNNs and DNNs) for autonomous steering using the (N+M) Evolution Strategy (ES) with the 1/5th success rule. The primary objective is to develop a lightweight CNN based model capable of real-time steering angle prediction, mimicking human driving behavior on predefined paths. The ES algorithm automates hyperparameter tuning, dynamically adjusting parameters such as filter sizes and layer configurations. Data collection encompasses driving scenarios recorded via the LTU ACTor autonomous driving platform, including variations in path direction and driving style. The very small dataset consists of timestam...
| Authors | Devson Butani, Ryan Kaddis, Chan-Jin Chung |
|---|---|
| Primary category | cs.NE |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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