It is critical for autonomous vehicles to accurately forecast the future trajectories of surrounding agents to avoid collisions. However, capturing the complex interactions between agents in complex urban scenes is challenging. As a result, complex interactions may impair trajectory prediction accuracy. A trajectory prediction network with an enhanced Graph Transformer (TP-EGT) is proposed to forecast the future trajectories of traffic-agents. A collision-aware Graph Transformer is introduced to capture the complex social interactions between traffic-agents. Following that, an additional interaction prediction task that could predict the interaction probabilities between agents is proposed to mitigate the over-smoothing issue of the Graph Transformer via a multi-task learning strategy. Afterward, the trajectory prediction performance is improved with additional interaction probabilities, which are beneficial for the decision-making and planning modules of autonomous vehicles. Quantitative and qualitative evaluations of TP-EGT on the ETH/UCY and ApolloScape databases demonstrate that the trajectory prediction accuracy of TP-EGT is comparable to the state-of-the-art baseline methods, and the predicted interaction probabilities can help autonomous vehicles comprehend the complex traffic scenes.

As a new round of technological revolution and industrial transformation accelerates, intelligent connected vehicles have emerged as carriers for advanced technology integration across industries and multiple fields. The intelligent cockpit serves as a comprehensive application space for various new technologies of intelligent connected vehicles, fostering integration among intelligent technology, products, and markets to form a virtuous circle. Developing intelligent cockpits helps to enhance the overall competitiveness and innovation capabilities of the industry, driving the growth of the intelligent automotive industry ecosystem. This paper proposes the definition of the intelligent cockpit for automobiles, classifying it into five levels from “functional cockpit” to “fully cognitive intelligent cockpit” based on the human-machine-environment perspective. The paper also provides a preliminary description of the “3-horizontal-3- vertical” technical architecture of the intelligent cockpit and outlines the development roadmap. Additionally, the paper proposes a comprehensive evaluation index system and method for assessing the intelligence of automotive cockpits. By addressing these key aspects, we hope to shed light on the potential of intelligent cockpits in advancing the field of intelligent connected vehicles.

High-precision vehicle trajectory prediction can enable autonomous vehicles to provide a safer and more comfortable trajectory planning and control. Unfortunately, current trajectory prediction methods have difficulty extracting hidden driving features across multiple time steps, which is important for long-term prediction. In order to solve this shortcoming, a temporal pattern attention-based trajectory prediction network, named TP2Net, was proposed, and vehicle of interest inception was established to construct an interaction model among vehicles. Experimental results show a 15% improvement in predictive performance over the previous best method under a 5-s prediction horizon. Moreover, in order to explain why temporal pattern attention was adopted and demonstrate its ability to extract hidden features that are intuitive to human beings, a layer interpretation module was included in TP2Net to quantify the mutual information contained between the input and the intermediate layer output tensor. The results of experiments using naturalistic trajectory datasets indicated that temporal pattern attention can extract three important stages in lane changing, showing that temporal pattern attention can effectively extract hidden features and improve prediction accuracy.