The representation form of multimodal motion prediction results is critical to the efficiency of the prediction-planning workflow. However, most existing methods generate multiple sets of trajectories for each independent agent to cover diverse modalities, overlooking interactions among agents during the prediction period, making it challenging to achieve scene-level consistency in multi-agent predictions. This paper introduces MliG, a scene-level multimodal conditional motion prediction framework based on Multi-Layer Interaction Graph. The interaction graph designed explicitly represent the interaction behaviors of multi-agents over future periods, guiding conditional trajectory prediction to achieve planning-friendly prediction result representation. First, an Interaction-Mask-based determination strategy is introduced to obtain ground truth interaction labels, enabling data-driven implicit relationship learning, and the generated training labels can support the accurate construction of the interaction graph. Second, scene simplification and modality decoupling using the interaction graph make it easier to fuse information and represent driving scenarios efficiently. Based on the multi-layer design, the complex interactions between agent pairs are decomposed into different group and modality layers, ensuring the diversity of interactions while improving the efficiency of conditional predictions. Extensive experiments on the Argoverse 2, Argoverse 1, and INTERACTION Datasets demonstrate that the proposed MliG achieves high prediction accuracy and significantly improves the scene-level consistency of trajectory prediction modalities for multi-agents in the same driving scenario.

As a critical technology to improve vehicle safety and handling stability, chassis coordinated control technology faces challenges in multi-system coordination, multi-variable solutions, and multi-task allocation. This paper combines the advantages of decentralized and distributed architectures and proposes a scenario-driven extension Hybrid-DMPC algorithm with variable topology and distributed modeling. Firstly, a two-dimensional extension coordinate is constructed with the$\beta - \dot {\beta } $phase plane and LTR as feature states. The correlation function is then solved to divide the vehicle states into classical domain, extension domain, and non-domain, forming a mapping relationship with the control architecture. Secondly, the distributed state space equations with state coupling and input coupling are constructed. The proposed algorithm is designed to employ decentralized, distributed, and hybrid architectures in the classical domain, non-domain, and extension domain, respectively. The correlation function determines the fusion rules for multiple control inputs in the extension domain. Thirdly, a cost-coupled weighted optimization function is designed, enabling local agents to coordinate global performance goals through iterative optimization. Finally, co-simulation and HIL test results verified that the proposed algorithm effectively coordinates multiple control objectives, achieving high-precision trajectory tracking, excellent handling stability, and anti-roll performance.

In autonomous driving (AD) tasks, data-driven deep reinforcement learning (DRL) outperforms rule-based methods in terms of continuous decision-making and adaptability. However, traditional DRL relies on hand-crafted reward functions, which introduce objective alignment challenges and reward loopholes. Moreover, the black-box structure makes it difficult to explain the decision-making process, which has a direct impact on DRL performance in complex driving situations. To address these shortcomings, a preference-based decomposable proximal policy optimization algorithm (PDPPO) is proposed for reliable interactive urban AD. The framework deconstructs the federated reinforcement learning (FRL) algorithm from various perspectives using a rule-based preference model, resulting in high-availability algorithmic performance for AD. PDPPO employs a data-rule fusion-driven hybrid vision transformer to overcome the objective alignment and high-dimensional state-space representation challenges of traditional DRL in complex urban traffic environments. Furthermore, to address the issue of algorithmic trustworthiness, PDPPO models the multi-agent FRL co-optimization process as an interpretable self-organized group collaboration process. This approach enables the algorithm to strike a balance between model robustness and sample efficiency using preference-heuristic parameter aggregation. The simulation results demonstrate that the proposed PDPPO algorithmic framework can implement interpretable single-agent decision control and multi-agent co-optimization processes. Furthermore, it exhibits competitive performance on various benchmark tests.

The accuracy and stability of motion prediction are crucial for the safe planning of autonomous driving systems. The widely used attention mechanisms effectively improve prediction accuracy. However, their computational cost grows quadratically with sequence length, presenting challenges for handling complex, large-scale scenarios. The attention patterns of motion prediction tasks exhibit significant data-related sparsity, indicating that not all scene elements are worth interaction. Therefore, this paper proposes a novel motion prediction framework that enhances the efficiency and stability of interaction fusion while achieving promising prediction accuracy. Firstly, an adaptive self-distillation attention module based on sparse interaction graph is designed. This module adaptively filters high-value sequences for each target to achieve sparse attention calculation and balance scene scale. Due to the lack of interaction labels in the original dataset, a self-distillation strategy is employed for model training. Secondly, a multi-stage dynamic anchor decoder is introduced that leverages the information filtering and aggregation capabilities of the sparse attention mechanism to improve prediction accuracy. The decoder dynamically updates the target anchor representations as the prediction progresses, ensuring consistency between interaction states and the prediction process in long-term forecasting. This approach effectively focuses attention calculation on the most relevant scene context fusion and trajectory decoding at each prediction stage. Validation results on Argoverse 1 and Argoverse 2 demonstrate that the proposed method achieves competitive accuracy while effectively reducing computational resource consumption. The proposed method can also serve as a plug-in that can be seamlessly incorporated into standard motion prediction pipelines to optimize scene interaction, making it more friendly to low-cost devices.

A detailed representation of the surrounding road scene is crucial for an autonomous driving system. yellow The camera-based Bird’s Eye View map has been a popular solution to present the surrounding information, due to its low cost and rich spatial context information. Most of the existing methods predict the BEV map based on the depth-estimation or the trivial homography method, which may cause the error propagation and the absence of content. To overcome these drawbacks, we propose a novel end-to-end framework that employs the front monocular image to predict the road layout and vehicle occupancy. In particular, to capture the long-range feature, we redesign a CNN encoder with a large kernel size to extract the image features. For reducing the big difference between the front image features and the top-down features, we propose a novel Spatial-Channel projection module to convert the front map into the top-down space. Additionally, concerning the correlation between front view and top-down view, we propose the Dual Cross-view Transformer module to refine the top-down view feature maps and strengthen the transformation. Extensive evaluations on the KITTI and Argoverse datasets present that the proposed model achieves the state-of-the-art results for both datasets. Furthermore, the proposed model runs in 37 FPS on a single GPU, demonstrating the generation of a real-time BEV map. The code will be published at https://github.com/raozhongyu/BEV_LKA.