STRAP: Spatial-Temporal Risk-Attentive Vehicle Trajectory Prediction for Autonomous Driving

• URL: http://arxiv.org/abs/2507.08563v2
• Date: Mon, 14 Jul 2025 08:04:31 GMT
• Title: STRAP: Spatial-Temporal Risk-Attentive Vehicle Trajectory Prediction for Autonomous Driving
• Abstract summary: We propose a novel spatial-temporal risk-attentive trajectory prediction framework that incorporates a risk potential field.<n>The proposed framework provides interpretable, risk-aware predictions, contributing to more robust decision-making for autonomous driving systems.
• Score: 0.968535561940627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate vehicle trajectory prediction is essential for ensuring safety and efficiency in fully autonomous driving systems. While existing methods primarily focus on modeling observed motion patterns and interactions with other vehicles, they often neglect the potential risks posed by the uncertain or aggressive behaviors of surrounding vehicles. In this paper, we propose a novel spatial-temporal risk-attentive trajectory prediction framework that incorporates a risk potential field to assess perceived risks arising from behaviors of nearby vehicles. The framework leverages a spatial-temporal encoder and a risk-attentive feature fusion decoder to embed the risk potential field into the extracted spatial-temporal feature representations for trajectory prediction. A risk-scaled loss function is further designed to improve the prediction accuracy of high-risk scenarios, such as short relative spacing. Experiments on the widely used NGSIM and HighD datasets demonstrate that our method reduces average prediction errors by 4.8% and 31.2% respectively compared to state-of-the-art approaches, especially in high-risk scenarios. The proposed framework provides interpretable, risk-aware predictions, contributing to more robust decision-making for autonomous driving systems.

Related papers

• Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving [46.91409391705346]
  Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving.<n>A fundamental bottleneck in current systems is the disconnect between highly expressive uncertainty modeling and interpretable, safe motion planning.<n>We propose a novel sample-conditioned differentiable planning framework that bridges this gap by explicitly incorporating diffusion-generated future trajectories into the optimization process.
  arXiv  Detail & Related papers  (2026-06-02T08:10:02Z)
• From Cues to Horizons: Dynamic Risk Horizon Profiling for Trajectory Prediction [2.014349431451644]
  We propose a risk horizon profiling (RHP) module that incorporates a continuous, learnable potential field model for risk-aware trajectory prediction.<n>The module calculates the spatial-temporal proximity of surrounding objects to profile risk distributions across future horizons.<n>Compared to the baseline methods, our framework achieves a 25.0% reduction in 5s RMSE on the highD dataset and a 29.1% reduction in 5s minFDE on SHRP2.
  arXiv  Detail & Related papers  (2026-05-30T19:17:24Z)
• Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments [15.293238760957722]
  We propose a unified map modeling and learning framework for partially observable environments.<n>The proposed framework offers a comprehensive and practical solution for risk-aware planning in partially observable environments.
  arXiv  Detail & Related papers  (2026-05-21T08:56:38Z)
• Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability [79.08785366532287]
  We propose a lightweight risk-gated reinforcement learning approximation for risk-sensitive control under partial observability.<n>We evaluate the approach in two safety-critical partially observable domains: automated glucose regulation and safety-constrained navigation.
  arXiv  Detail & Related papers  (2026-05-14T01:23:09Z)
• Risk Estimation for Automated Driving [0.9376581451563295]
  In automated driving, risk is characterized by two aspects.<n>The uncertainty aspect typically causes the risk to be non-zero for near-collision events.<n>Existing approaches to calculate the risk either resort to empirical modeling or severe approximations.<n>In this paper, we combine recent advances in collision probability estimation with the concept of collision severity to develop a general method for accurate risk estimation.
  arXiv  Detail & Related papers  (2026-01-21T14:14:50Z)
• Beta Distribution Learning for Reliable Roadway Crash Risk Assessment [21.371420424228077]
  Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP.<n>Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment.<n>We introduce a novel deep learning framework that leverages satellite imagery as a comprehensive spatial input.<n>This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks.
  arXiv  Detail & Related papers  (2025-11-07T00:08:55Z)
• RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios [6.024186631622774]
  RiskNet is an interaction-aware risk forecasting framework for autonomous vehicles.<n>It integrates deterministic risk modeling with probabilistic behavior prediction for comprehensive risk assessment.<n>It supports real-time, scenario-adaptive risk forecasting and demonstrates strong generalization across uncertain driving environments.
  arXiv  Detail & Related papers  (2025-04-22T02:36:54Z)
• Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios [25.16311876790003]
  This paper proposes a risk-aware trajectory prediction framework tailored to safety-critical scenarios. We introduce a safety-critical trajectory prediction dataset and tailored evaluation metrics. Results demonstrate the superior performance of our model, with a significant improvement in most metrics.
  arXiv  Detail & Related papers  (2024-07-18T13:00:01Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving [1.8047694351309207]
  Risk assessment is a central element for the development and validation of Autonomous Vehicles. Time Headway (TH) and Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. We present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties.
  arXiv  Detail & Related papers  (2023-03-13T15:22:51Z)
• Mathematical Models of Human Drivers Using Artificial Risk Fields [8.074019565026544]
  We use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. We observe that the risk fields are excellent at predicting future trajectory with high prediction accuracy for up to twenty seconds prediction horizons.
  arXiv  Detail & Related papers  (2022-05-24T15:39:01Z)
• Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways [18.18842948832662]
  This paper introduces a prediction-based collision risk assessment approach on highways. We develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.
  arXiv  Detail & Related papers  (2022-05-03T07:58:02Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.