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01.07.2021 · Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) …

Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles.

In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the ...

Self-driving vehicles need to continuously analyse the driving scene, understand the behavior of other road users and predict their future trajectories in ...

Dec 20, 2021 · Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction ...

3) Prediction: Vehicle intent prediction is divided into two main aspects: maneuver [4], [23] and trajectory prediction [5], [24], [8]. The former generates a high-level representation of the motion such as lane changing and lane keeping. The latter outputs the predicted state over time. Different forms of outputs are used in the motion ...

A. Vehicle Trajectory Prediction Using Traditional Methods Conventionally, three types of approaches exist for ve-hicle trajectory prediction: physics-based, maneuver-based, and interaction-aware [14]. Physics-based methods usually consider vehicle kinematic and dynamic constraints such as

Feb 07, 2021 · "Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models" (IEEE Transactions on Industrial Electronics 2017) "Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network" (ITSC 2017)

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to ...

Enlightenment is expected for researchers seeking to improve trajectory prediction performance based on the achievement we have made. Comments: ...

The long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles: for example, a vehicle equipped with ...

25 rader · Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and …

Dec 11, 2017 · Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the ...

Accurate vehicle trajectory prediction can benefit a variety of Intelligent Transportation System applications ranging from traffic simulation to driver ...

07.02.2021 · "Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models" (IEEE Transactions on Industrial Electronics 2017) "Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network" (ITSC 2017) "Sequence-to ...

07.11.2020 · An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers.

08.10.2021 · Autonomous vehicles need to detect and analyze the movement of surrounding vehicles to make safe driving decisions. This paper proposes a Clustering Convolution-LSTM (CC-LSTM) vehicle trajectory prediction model which is made up two modules: Clustering module and Convolution-LSTM (C-LSTM) module.

20.12.2021 · Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks.

Trajectory Prediction. 115 papers with code • 24 benchmarks • 22 datasets. Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors ...

11.12.2017 · Abstract: Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches.

Apr 01, 2021 · In this paper, a trajectory prediction algorithm based on deep learning is proposed to solve the problem of vehicle trajectory prediction. In this paper, the vehicle passing records are preprocessed and the vehicle trajectory is generated. The trajectory is transformed into the discrete position sequence of the vehicle as the input of the ...