LMR: Lane Distance-Based Metric for Trajectory Prediction

• URL: http://arxiv.org/abs/2304.05869v2
• Date: Thu, 13 Apr 2023 07:22:48 GMT
• Title: LMR: Lane Distance-Based Metric for Trajectory Prediction
• Authors: Julian Schmidt, Thomas Monninger, Julian Jordan, Klaus Dietmayer
• Abstract summary: Currently established metrics are based on Euclidean distance, which means that errors are weighted equally in all directions. We propose a new metric that is lane distance-based: Lane Miss Rate (LMR) LMR is defined as the ratio of sequences that yield a miss.
• Score: 10.83642398981694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, which means that errors are weighted equally in all directions. Euclidean metrics are insufficient for structured environments like roads, since they do not properly capture the agent's intent relative to the underlying lane. In order to provide a reasonable assessment of trajectory prediction approaches with regard to the downstream planning task, we propose a new metric that is lane distance-based: Lane Miss Rate (LMR). For the calculation of LMR, the ground-truth and predicted endpoints are assigned to lane segments, more precisely their centerlines. Measured by the distance along the lane segments, predictions that are within a certain threshold distance to the ground-truth count as hits, otherwise they count as misses. LMR is then defined as the ratio of sequences that yield a miss. Our results on three state-of-the-art trajectory prediction models show that LMR preserves the order of Euclidean distance-based metrics. In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes. Hits on the other hand result for sequences where predictions are located on the correct lane. This means that LMR implicitly weights Euclidean error relative to the lane and goes into the direction of capturing intents of traffic agents. The source code of LMR for Argoverse 2 is publicly available.

Related papers

• Intrinsic Bayesian Cramér-Rao Bound with an Application to Covariance Matrix Estimation [49.67011673289242]
  This paper presents a new performance bound for estimation problems where the parameter to estimate lies in a smooth manifold. It induces a geometry for the parameter manifold, as well as an intrinsic notion of the estimation error measure.
  arXiv  Detail & Related papers  (2023-11-08T15:17:13Z)
• CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving [6.868387710209245]
  We propose a new benChmarking paRadIgm for evaluaTing trajEctoRy predIction Approaches (CRITERIA) We show that the proposed benchmark can produce a more accurate ranking of the models and serve as a means of characterizing their behavior.
  arXiv  Detail & Related papers  (2023-10-11T18:28:15Z)
• Decoupling the Curve Modeling and Pavement Regression for Lane Detection [67.22629246312283]
  curve-based lane representation is a popular approach in many lane detection methods. We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
  arXiv  Detail & Related papers  (2023-09-19T11:24:14Z)
• DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport [23.189529003370303]
  Real-time LiDAR-based odometry is imperative for many applications like robot navigation, globally consistent 3D scene map reconstruction, or safe motion-planning. We introduce a novel deep learning-based real-time (approx. 35-40ms per frame) LO method that jointly learns accurate frame-to-frame correspondences and model's predictive uncertainty (PU) as evidence to safe-guard LO predictions. We evaluate our method on KITTI dataset and show competitive performance, even superior generalization ability over recent state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-08-14T14:06:21Z)
• Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? [135.71855998537347]
  We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy. We show that this metric is unreliable, as even vacuous blind classifiers can achieve unrealistically high online accuracy. Existing OCL algorithms can also achieve high online accuracy, but perform poorly in retaining useful information.
  arXiv  Detail & Related papers  (2023-05-16T08:29:33Z)
• Joint Metrics Matter: A Better Standard for Trajectory Forecasting [67.1375677218281]
  Multi-modal trajectory forecasting methods evaluate using single-agent metrics (marginal metrics) Only focusing on marginal metrics can lead to unnatural predictions, such as colliding trajectories or diverging trajectories for people who are clearly walking together as a group. We present the first comprehensive evaluation of state-of-the-art trajectory forecasting methods with respect to multi-agent metrics (joint metrics): JADE, JFDE, and collision rate.
  arXiv  Detail & Related papers  (2023-05-10T16:27:55Z)
• What's Wrong with the Absolute Trajectory Error? [14.533304890042361]
  In this work, we propose an alternative error metric for evaluating the accuracy of the reconstructed camera trajectory. Our metric, named Discernible Trajectory Error (DTE), is computed in five steps. We also propose a novel rotation error metric, named Discernible Rotation Error (DRE), which has similar advantages to the DTE.
  arXiv  Detail & Related papers  (2022-12-10T23:48:27Z)
• Improving Diversity of Multiple Trajectory Prediction based on Map-adaptive Lane Loss [12.963269946571476]
  This study proposes a novel loss function, textitLane Loss, that ensures map-adaptive diversity and accommodates geometric constraints. Experiments performed on the Argoverse dataset show that the proposed method significantly improves the diversity of the predicted trajectories.
  arXiv  Detail & Related papers  (2022-06-17T09:09:51Z)
• Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation [99.54200992904721]
  We propose a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) to jointly learn the evolutionary patterns of OD and DO ridership. An OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices. A DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously.
  arXiv  Detail & Related papers  (2021-07-02T10:11:51Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)
• Theoretical Guarantees for Bridging Metric Measure Embedding and Optimal Transport [18.61019008000831]
  We consider a method allowing to embed the metric measure spaces in a common Euclidean space and compute an optimal transport (OT) on the embedded distributions. This leads to what we call a sub-embedding robust Wasserstein (SERW) distance.
  arXiv  Detail & Related papers  (2020-02-19T17:52:01Z)

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.