  The performance of the current collision avoidance systems in Autonomous
Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically
affected by low light and adverse weather conditions. Collisions with large
animals such as deer in low light cause significant cost and damage every year.
In this paper, we propose the first AI-based method for future trajectory
prediction of large animals and mitigating the risk of collision with them in
low light. In order to minimize false collision warnings, in our multi-step
framework, first, the large animal is accurately detected and a preliminary
risk level is predicted for it and low-risk animals are discarded. In the next
stage, a multi-stream CONV-LSTM-based encoder-decoder framework is designed to
predict the future trajectory of the potentially high-risk animals. The
proposed model uses camera motion prediction as well as the local and global
context of the scene to generate accurate predictions. Furthermore, this paper
introduces a new dataset of FIR videos for large animal detection and risk
estimation in real nighttime driving scenarios. Our experiments show promising
results of the proposed framework in adverse conditions. Our code is available
online.
