Interaction-Aware Intention Estimation at Roundabouts

Abstract

Roundabouts have many benefits when compared with traditional signal-controlled intersections: improve safety, reduce delay, improve traffic flow, are less expensive and occupy less area. The behavior of traffic participants is full of uncertainties in the real world. An automated system that relies only on its perception is unable to safely enter the roundabout until a large gap occurs or the vehicle approaching has actually left the roundabout or passed the conflict area. In order to improve the driving quality, autonomous vehicles should be able to infer the correct intention at roundabout as early as possible. In this work, a method to classify the intentions of the surrounding vehicles at unsignalized roundabouts is proposed. For each vehicle at the scene, a Dynamic Bayesian Network is instantiated and the intentions are inferred using a particle filter.

GPU-Accelerated Interaction-Aware Motion Prediction

“…These interactions are computed for every vehicle in the scene against the rest and take into account both information that is coded into the traffic rules and also potential deviations from the expected road-safe behavior to deal with unsafe driving situations, such as violating a stop line. This feature makes this interaction-aware motion prediction algorithm especially useful in complex situations with dense traffic, like unsignalized intersections [20], sudden unsafe lane changes [21], or roundabouts [22]. The intentions are then fused with the motion predictions computed with a kinematic model to produce a 3D motion grid used by the ego vehicle to navigate through the scene.…”

“…The main parts where the GPU-based acceleration has significantly reduced the computation time are highlighted in blue in the flowchart, namely in the particle filter used to compute the intentions and in the matrix multiplication of the motion prediction. This flowchart is extensively described in [20][21][22], where the algorithm is validated and compared with state-of-the-art approaches. Hence, it will only be briefly reviewed below.…”