arXiv:1811.04453
Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework
원문 초록
We propose a new deep learning based framework to identify pedestrians, and caution distracted drivers, in an effort to prevent the loss of life and property. This framework uses two Convolutional Neural Networks (CNN), one which detects pedestrians and the second which predicts the onset of drowsiness in a driver, is implemented on a Raspberry Pi 3 Model B+, shows great promise. The algorithm for implementing such a low-cost, low-compute model is presented and the results discussed.
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ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone
Risk-Aware Robot Control in Dynamic Environments Using Belief Control Barrier Functions
AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation
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