Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques

• URL: http://arxiv.org/abs/2109.10656v1
• Date: Wed, 22 Sep 2021 11:21:20 GMT
• Title: Vehicle Behavior Prediction and Generalization Using Imbalanced Learning Techniques
• Authors: Theodor Westny, Erik Frisk, and Bj\"orn Olofsson
• Abstract summary: This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Evaluations show that the method enhances model performance, resulting in improved classification accuracy.
• Score: 1.3381749415517017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of learning-based methods for vehicle behavior prediction is a promising research topic. However, many publicly available data sets suffer from class distribution skews which limits learning performance if not addressed. This paper proposes an interaction-aware prediction model consisting of an LSTM autoencoder and SVM classifier. Additionally, an imbalanced learning technique, the multiclass balancing ensemble is proposed. Evaluations show that the method enhances model performance, resulting in improved classification accuracy. Good generalization properties of learned models are important and therefore a generalization study is done where models are evaluated on unseen traffic data with dissimilar traffic behavior stemming from different road configurations. This is realized by using two distinct highway traffic recordings, the publicly available NGSIM US-101 and I80 data sets. Moreover, methods for encoding structural and static features into the learning process for improved generalization are evaluated. The resulting methods show substantial improvements in classification as well as generalization performance.

Related papers

• A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification [51.35500308126506]
  Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. We study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types.
  arXiv  Detail & Related papers  (2024-07-16T23:17:36Z)
• Self-Supervised Representation Learning with Meta Comprehensive Regularization [11.387994024747842]
  We introduce a module called CompMod with Meta Comprehensive Regularization (MCR), embedded into existing self-supervised frameworks. We update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features. We provide theoretical support for our proposed method from information theory and causal counterfactual perspective.
  arXiv  Detail & Related papers  (2024-03-03T15:53:48Z)
• Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep-Learning and Interpolators [6.537685198688539]
  We present a methodology for using unlabeled data to design semi supervised learning (SSL) methods. We include in each of them a mixing parameter $alpha$, controlling the weight given to the unlabeled data. We demonstrate the effectiveness of our methodology in delivering substantial improvement compared to the standard supervised models.
  arXiv  Detail & Related papers  (2023-02-19T09:55:18Z)
• Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach [90.77858044524544]
  We reformulate the recognition probabilities through included angles without re-balancing the classifier weights. Inspired by the performance improvement of the predictive form reformulation, we explore the different properties of this angular prediction. Our method is able to obtain the best performance among peer methods without pretraining on CIFAR10/100-LT and ImageNet-LT.
  arXiv  Detail & Related papers  (2022-12-03T07:52:48Z)
• IDM-Follower: A Model-Informed Deep Learning Method for Long-Sequence Car-Following Trajectory Prediction [24.94160059351764]
  Most car-following models are generative and only consider the inputs of the speed, position, and acceleration of the last time step. We implement a novel structure with two independent encoders and a self-attention decoder that could sequentially predict the following trajectories. Numerical experiments with multiple settings on simulation and NGSIM datasets show that the IDM-Follower can improve the prediction performance.
  arXiv  Detail & Related papers  (2022-10-20T02:24:27Z)
• SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models [58.78043959556283]
  We study the behaviors of models under different fine-tuning methodologies, including full fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning methods. Our analysis uncovers that LoRA fine-tuning consistently exhibits significantly better OOD generalization performance than full fine-tuning across various scenarios.
  arXiv  Detail & Related papers  (2022-10-10T16:07:24Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• A Gating Model for Bias Calibration in Generalized Zero-shot Learning [18.32369721322249]
  Generalized zero-shot learning (GZSL) aims at training a model that can generalize to unseen class data by only using auxiliary information. One of the main challenges in GZSL is a biased model prediction toward seen classes caused by overfitting on only available seen class data during training. We propose a two-stream autoencoder-based gating model for GZSL.
  arXiv  Detail & Related papers  (2022-03-08T16:41:06Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder [23.860842627883187]
  We teach the model to capture broader variations of the feature distributions with a novel noise-enhanced supervised autoencoder (NSAE) NSAE trains the model by jointly reconstructing inputs and predicting the labels of inputs as well as their reconstructed pairs. We also take advantage of NSAE structure and propose a two-step fine-tuning procedure that achieves better adaption and improves classification performance in the target domain.
  arXiv  Detail & Related papers  (2021-08-11T04:45:56Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)

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.