Summary of Driver Fatigue Prediction Using Randomly Activated Neural Networks For Smart Ridesharing Platforms, by Sree Pooja Akula et al.
Driver Fatigue Prediction using Randomly Activated Neural Networks for Smart Ridesharing Platforms
by Sree Pooja Akula, Mukund Telukunta, Venkata Sriram Siddhardh Nadendla
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Dynamic Discounted Satisficing (DDS) heuristic models driver’s sequential ride decisions during a given shift in ridesharing platforms. A novel stochastic neural network with random activations is used to predict final decisions made by drivers. The network requires a new training algorithm, Sampling-Based Back Propagation Through Time (SBPTT), which aggregates gradients from independent instances of neural networks. Simulation experiments and real-world data from the Chicago taxi dataset demonstrate improved performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ridesharing platforms can be affected by driver fatigue and cognitive atrophy throughout the day. This paper presents a new way to model how drivers make decisions about which rides to accept. A special type of neural network is used, called stochastic neural networks with random activations. This requires a unique training method. The results show that this approach works better than other methods when tested using simulations and real-world data from the Chicago taxi dataset. |
Keywords
» Artificial intelligence » Neural network
