  Autonomous driving holds great promise in addressing traffic safety concerns
by leveraging artificial intelligence and sensor technology. Multi-Object
Tracking plays a critical role in ensuring safer and more efficient navigation
through complex traffic scenarios. This paper presents a novel deep
learning-based method that integrates radar and camera data to enhance the
accuracy and robustness of Multi-Object Tracking in autonomous driving systems.
The proposed method leverages a Bi-directional Long Short-Term Memory network
to incorporate long-term temporal information and improve motion prediction. An
appearance feature model inspired by FaceNet is used to establish associations
between objects across different frames, ensuring consistent tracking. A
tri-output mechanism is employed, consisting of individual outputs for radar
and camera sensors and a fusion output, to provide robustness against sensor
failures and produce accurate tracking results. Through extensive evaluations
of real-world datasets, our approach demonstrates remarkable improvements in
tracking accuracy, ensuring reliable performance even in low-visibility
scenarios.
