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What If Self-Driving Cars Could Predict Accidents Before They Happen? Here’s How We Did It
- Cloud at Cal developed a next-frame prediction model and early warning system to predict safe driving conditions, detect deviations, and alert human drivers.
- Recurrent Neural Networks (RNNs) process sequential data, while Long Short-Term Memory (LSTM) models address long-term dependencies.
- Convolutional LSTM (ConvLSTM) models combine LSTM and CNN strengths for spatial and temporal pattern recognition.
- Over 160GB of dashcam footage was scraped for training the ConvLSTM model, including diverse driving conditions.
- An AWS-based architecture was used for real-time hazard prediction with Amazon Kinesis for ingestion and Sagemaker for training.
- Anomaly detection compares predicted frames with real-time frames to flag potential hazards.
- Amazon SNS sends alerts for potential hazards, allowing drivers to intervene and prevent risks.
- Future work includes improving input analysis, prediction accuracy, and model latency for enhanced performance.
- Exploration of AWS Rekognition Video, pre-trained models, and ViTs could lead to improved system accuracy.
- Model quantization and distillation techniques are planned to reduce model latency for real-time inference.
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