Precise trajectory prediction of surrounding vehicles is critical for decision-making autonomous and learning-based approaches are well recognized the robustness. However, state-of-the-art methods ignore 1) feasibility vehicle's multi-modal state information 2) mutual exclusive relationship between global traffic scene receptive fields local position resolution when modeling vehicles' interacti...

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve complex non-convex problem. As result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be potential solution but is computationally intractable in real-time setting. In this letter, we present MPC capable searching over different driving ...

With the increasing prevalence of location-aware devices, trajectory data has been generated and collected in various application domains. Trajectory data carries rich information that is useful for many data analysis tasks. Yet, improper publishing and use of trajectory data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving trajectory data publish...

We’ve been developing a sensor that can acquire positional data. Recently, a position-based big data creation is easy task and trajectory analysis is the highest priority for ”position-based service”. Traffic congestion, marketing mining, and pattern analysis are the one of the examples in trajectory analysis field. In this paper, we propose the trajectory analysis approach for clustering and a...

Predicting the trajectories of surrounding heterogeneous traffic agents is critical for decision making an autonomous vehicle. Recently, many existing prediction methods have focused on capturing interactions between to improve accuracy. However, few pay attention temporal dependencies that there are different behavioural at time scales. In this work, authors propose a novel framework trajector...

Devices like smart phones and GPS navigators are very popular nowadays. These equipments can save the location of an object with an associated time, generating a new kind of data, called trajectories of moving objects. With these data it is possible to discover several interesting patterns, among which is the interaction between individuals, allowing to infer their relationship. This work addre...

Studding the movement of animals and their behavior become an important subject. Indeed, such study can generate useful information used next to assist the decision makers involved in different domains such as agriculture, security, health care and business. We present in this work a trajectory model related to the displacement of the herd, which allows the generation of trajectory data that is...

Analyzing trajectory data can provide people with a higher quality of life. However, publishing directly will leak privacy. The authors propose publication method based on differential privacy (TDDP). TDDP consists two stages. In the location generalization stage, firstly, locations at each timestamp are clustered into classes by k-means++ algorithm, and then representative class is selected us...

Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based model that emulates the human’s decision-making mechanism utilized. To infer function parameters from naturalistic human data, we propose a structural assumption about focuses on discrete latent intentions. It converts continuo...