FP-TTC: Fast Prediction of Time-to-Collision using Monocular Images

Time-to-Collision (TTC) is a measure of the time until an object collides with the observation plane which is a critical input indicator for obstacle avoidance and other downstream modules. Previous works have utilized deep neural networks to estimate TTC with monocular cameras in an end-to-end manner, which obtain the state-of-the-art (SOTA) accuracy performance. However, these models usually have deep layers and numerous parameters, resulting in long inference time and high computational overhead. Moreover, existing methods use two frames which are the current and future moments as input to calculate the TTC resulting in a delay during the calculation process. To solve these issues, we propose a novel fast TTC prediction model: FP-TTC. We first use an attention-based scale encoder to model the scale-matching process between images, which significantly reduces the computational overhead as well as improves the model's accuracy. Meanwhile, a simple but powerful trick is introduced to the model, where we built a time-series decoder and predict the current TTC from RGB images in the past, avoiding the computational delay caused by the system time step interval, and further improved the TTC prediction speed. Our model achieves a parameter reduction of 89.1%, a 5.5-fold increase in inference speed, a 19.3% improvement in accuracy. We also provided a lightweight version of FP-TTC, which further optimized the inference speed and parameter count by 15%. Our code is available at https://github.com/LChanglin/FP-TTC.