Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

• URL: http://arxiv.org/abs/2003.00759v2
• Date: Sat, 5 Sep 2020 09:48:09 GMT
• Title: Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
• Authors: Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Junqiang Xi
• Abstract summary: Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) processes. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehiclepedestrian interactions.
• Score: 11.893098067272463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.

Related papers

• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• Diffusion-Based Environment-Aware Trajectory Prediction [3.1406146587437904]
  The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data.
  arXiv  Detail & Related papers  (2024-03-18T10:35:15Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction [4.181632607997678]
  We propose a hierarchical hybrid framework of deep learning (DL) and reinforcement learning (RL) for multi-agent trajectory prediction. In the DL stage, the traffic scene is divided into multiple intermediate-scale heterogenous graphs based on which Transformer-style GNNs are adopted to encode heterogenous interactions. In the RL stage, we divide the traffic scene into local sub-scenes utilizing the key future points predicted in the DL stage.
  arXiv  Detail & Related papers  (2023-03-22T02:47:42Z)
• Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction [29.701029725302586]
  We study the joint trajectory prediction problem with the goal-conditioned framework. We introduce a conditional-variational-autoencoder-based (CVAE) model to explicitly encode different interaction modes into the latent space. We propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
  arXiv  Detail & Related papers  (2022-03-28T21:41:21Z)
• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields [8.744017403796406]
  We develop a model for learning heterogeneous and dynamic patterns of velocity field data. We show the effectiveness of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
  arXiv  Detail & Related papers  (2021-02-15T17:45:46Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle [58.720142291102135]
  Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing. Existing outside-the-vehicle referencing methods focus on a static situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. We investigate the specific characteristics of each modality and the interaction between them when used in the task of referencing outside objects.
  arXiv  Detail & Related papers  (2020-09-23T14:56:19Z)
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
  In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
  arXiv  Detail & Related papers  (2020-07-23T14:31:25Z)
• MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic [35.22312783822563]
  Trajectory prediction in urban mixedtraffic zones is critical for many intelligent transportation systems. We propose an approach named Multi-Context Network (MCENET) that is trained by encoding both past and future scene context. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories.
  arXiv  Detail & Related papers  (2020-02-14T11:04:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.