Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving

• URL: http://arxiv.org/abs/2512.12211v1
• Date: Sat, 13 Dec 2025 06:48:32 GMT
• Title: Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving
• Authors: Longchao Da, David Isele, Hua Wei, Manish Saroya,
• Abstract summary: We propose a comprehensive pipeline that adaptively evaluates the predictor's performance by two dimensions: accuracy and diversity.<n>Experiments on a closed-loop benchmark using real-world datasets show that our pipeline yields a more reasonable evaluation than traditional metrics.
• Score: 8.115144579497644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Being able to anticipate the motion of surrounding agents is essential for the safe operation of autonomous driving systems in dynamic situations. While various methods have been proposed for trajectory prediction, the current evaluation practices still rely on error-based metrics (e.g., ADE, FDE), which reveal the accuracy from a post-hoc view but ignore the actual effect the predictor brings to the self-driving vehicles (SDVs), especially in complex interactive scenarios: a high-quality predictor not only chases accuracy, but should also captures all possible directions a neighbor agent might move, to support the SDVs' cautious decision-making. Given that the existing metrics hardly account for this standard, in our work, we propose a comprehensive pipeline that adaptively evaluates the predictor's performance by two dimensions: accuracy and diversity. Based on the criticality of the driving scenario, these two dimensions are dynamically combined and result in a final score for the predictor's performance. Extensive experiments on a closed-loop benchmark using real-world datasets show that our pipeline yields a more reasonable evaluation than traditional metrics by better reflecting the correlation of the predictors' evaluation with the autonomous vehicles' driving performance. This evaluation pipeline shows a robust way to select a predictor that potentially contributes most to the SDV's driving performance.

Related papers

• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach [22.81464823797471]
  HiT (Human-like Trajectory Prediction) is a novel model designed to enhance trajectory prediction by incorporating behavior-aware modules and dynamic centrality measures.<n>To evaluate HiT's performance, we conducted extensive experiments using diverse and challenging real-world datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:04:01Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  We present nuPlan, a real-world motion planning benchmark that captures multi-agent interactions.<n>We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN)<n>We also present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Probabilistic Prediction of Longitudinal Trajectory Considering Driving Heterogeneity with Interpretability [12.929047288003213]
  This study proposes a trajectory prediction framework that combines Mixture Density Networks (MDN) and considers the driving heterogeneity to provide probabilistic and personalized predictions. The proposed framework is tested based on a wide-range vehicle trajectory dataset.
  arXiv  Detail & Related papers  (2023-12-19T12:56:56Z)
• What Truly Matters in Trajectory Prediction for Autonomous Driving? [25.700880872677256]
  Trajectory prediction plays a vital role in the performance of autonomous driving systems. A significant disparity exists between the accuracy of predictors on fixed datasets and driving performance when the predictors are used downstream for vehicle control.
  arXiv  Detail & Related papers  (2023-06-27T01:29:20Z)
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
  Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors. We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories. Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
  arXiv  Detail & Related papers  (2022-09-19T03:34:59Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• Rethinking Trajectory Forecasting Evaluation [42.228191984697006]
  We take a step back and critically evaluate current trajectory forecasting metrics. We propose task-aware metrics as a better measure of performance in systems where prediction is being deployed.
  arXiv  Detail & Related papers  (2021-07-21T18:20:03Z)
• Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning [23.321627835039934]
  We propose prediction by anticipation, which views interaction in terms of a latent probabilistic generative process. Under this view, consecutive data frames can be factorized into sequential samples from an action-conditional distribution. Our proposed prediction model, variational Bayesian in nature, is trained to maximize the evidence lower bound (ELBO) of this conditional distribution.
  arXiv  Detail & Related papers  (2020-12-25T01:39:26Z)
• 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.