Multi-Object Tracking with Interacting Vehicles and Road Map Information
This work addresses tracking challenges for autonomous vehicles in complex traffic, but it is incremental as it adapts existing methods to incorporate interaction and road constraints.
The paper tackles the problem of multi-object tracking in traffic scenarios where vehicles interact and road geometry restricts motion, by extending a Labeled Multi-Bernoulli filter with the Intelligent Driver Model and road map approximations. The result is higher accuracy and robustness in track estimations when standard motion models fail.
In many applications, tracking of multiple objects is crucial for a perception of the current environment. Most of the present multi-object tracking algorithms assume that objects move independently regarding other dynamic objects as well as the static environment. Since in many traffic situations objects interact with each other and in addition there are restrictions due to drivable areas, the assumption of an independent object motion is not fulfilled. This paper proposes an approach adapting a multi-object tracking system to model interaction between vehicles, and the current road geometry. Therefore, the prediction step of a Labeled Multi-Bernoulli filter is extended to facilitate modeling interaction between objects using the Intelligent Driver Model. Furthermore, to consider road map information, an approximation of a highly precise road map is used. The results show that in scenarios where the assumption of a standard motion model is violated, the tracking system adapted with the proposed method achieves higher accuracy and robustness in its track estimations.