A Dual Learning Model for Vehicle Trajectory Prediction

Abstract

Automated vehicles and advanced driver-assistance systems require an accurate prediction of future traffic scene states. The tendency in recent years has been to use deep learning approaches for accurate trajectory prediction but these approaches suffer from computational complexity, dependency on a specific environment/dataset, and lack of insight into vehicle interactions. In this paper, we aim to address these limitations by proposing a Dual Learning Model (DLM) using lane occupancy and risk maps for vehicle trajectory prediction. To understand the spatial interactions of road users, make the model independent of the environment, and consider inter-vehicle distances, we embed an Occupancy Map (OM) into the trajectory prediction model. We also utilise a traffic scene Risk Map (RM) to explicitly consider a comprehensive definition of risk based on Time-to-Collision in the traffic scene. These two features employed in the encoder-decoder architecture improve system accuracy with less complexity and provide insight into the interaction between all road users. The experiment has been conducted on two different naturalistic highway driving datasets (i.e., NGSIM and HighD) demonstrating algorithm independence from a single environment. Comparison results indicate that the DLM achieves a more accurate trajectory prediction with a less complex structure compared with existing approaches in terms of RMS prediction error, which indicates the effectiveness of DLM in such a context. INDEX TERMS Vehicle trajectory, trajectory prediction, recurrent neural network, deep feature learning, and long-short-term memory.

Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

“…e comparison results show that the proposed knowledge-driven LSTM network has better performance in RMSE for NGSIM. Note that as compared to the baseline [24], the prediction accuracy gets a slight improvement, but the proposed method enhances the real-time performance and much reduces the computational complexity due to the reduction of the feature space dimension.…”

“…e results show that the proposed method outperforms the state-of-the-art model and decreases the overall RMSE value of the system by 10 percent on average. Table 2 summarizes the RMSE values comparing the proposed methods with the baseline trajectory prediction models in the literature [20,[22][23][24]35].…”

“…Dai et al [23] proposed a spatiotemporal LSTM-based model, which considers the spatial interactions of the surrounding vehicles, but the constraints of other prior knowledge such as road structure, traffic rules, and driving experience are not considered. e dual learning model (DLM) which takes information from two different inputs to predict vehicle trajectory was presented in [24]. is model embeds the occupancy map and risk map into the trajectory model to consider a comprehensive definition of risk in the traffic scene, but the computational complexity usually grows exponentially if the dimensionality of the feature space increases.…”

Fatigue Performance of Steel-Concrete Composite Continuous Box Girder Bridge Deck

“…Vehicles do not drive exactly along the track line. Additionally, the distribution of the wheel track line along the transverse direction varies significantly among different countries; thus, it cannot be simply applied [52]. e Dazhenggang Bridge is a one-way four-lane bridge.…”

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions

“…By contrast, maneuver-based approaches (e. g. [1], [17]- [22]) try to infer the maneuver a driver intends to perform. Finally, interaction-aware approaches [23]- [26] provide the most advanced motion models by predicting the motions of all vehicles in a given situation simultaneously. In particular, these models consider that all vehicles mutually influence each other.…”