On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models

• URL: http://arxiv.org/abs/2206.00608v1
• Date: Wed, 1 Jun 2022 16:25:28 GMT
• Title: On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models
• Authors: Marvin Klingner, Konstantin M\"uller, Mona Mirzaie, Jasmin Breitenstein, Jan-Aike Term\"ohlen, Tim Fingscheidt
• Abstract summary: We investigate the influence of several data design choices regarding training and validation of deep driving models trainable in an end-to-end fashion. We show by correlation analysis, which validation design enables the driving performance measured during validation to generalize to unknown test environments.
• Score: 32.381828309166195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of data-driven machine learning (ML) has facilitated significant progress in many complicated tasks such as highly-automated driving. While much effort is put into improving the ML models and learning algorithms in such applications, little focus is put into how the training data and/or validation setting should be designed. In this paper we investigate the influence of several data design choices regarding training and validation of deep driving models trainable in an end-to-end fashion. Specifically, (i) we investigate how the amount of training data influences the final driving performance, and which performance limitations are induced through currently used mechanisms to generate training data. (ii) Further, we show by correlation analysis, which validation design enables the driving performance measured during validation to generalize well to unknown test environments. (iii) Finally, we investigate the effect of random seeding and non-determinism, giving insights which reported improvements can be deemed significant. Our evaluations using the popular CARLA simulator provide recommendations regarding data generation and driving route selection for an efficient future development of end-to-end driving models.

Related papers

• VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation [0.0]
  This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The model pre-trained by VIRL shows substantial enhancements on demonstrating improved generalizability with limited data.
  arXiv  Detail & Related papers  (2024-06-18T05:30:26Z)
• Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving [0.0]
  We propose methods for clustering trajectory-states and sampling strategies in an active learning framework. By integrating trajectory-state-informed active learning, we demonstrate that more efficient and robust autonomous driving systems are possible.
  arXiv  Detail & Related papers  (2024-05-15T02:54:11Z)
• Guiding Attention in End-to-End Driving Models [49.762868784033785]
  Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. We study how to guide the attention of these models to improve their driving quality by adding a loss term during training. In contrast to previous work, our method does not require these salient semantic maps to be available during testing time.
  arXiv  Detail & Related papers  (2024-04-30T23:18:51Z)
• SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control [59.20038082523832]
  We present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We develop a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data.
  arXiv  Detail & Related papers  (2024-03-28T14:07:13Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [71.63186089279218]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• An active inference model of car following: Advantages and applications [6.905724739762358]
  Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability.
  arXiv  Detail & Related papers  (2023-03-27T13:39:26Z)
• Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field [4.272601420525791]
  imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. Previous IL works show sample inefficiency and low generalisation in safety-critical scenarios, on which they are rarely tested. We present an IL model using the spline coefficient parameterisation and offline expert queries to enhance safety and training efficiency.
  arXiv  Detail & Related papers  (2022-10-04T17:07:35Z)
• The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations [0.0]
  We investigate the composition of training data in vehicle trajectory prediction. We show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set.
  arXiv  Detail & Related papers  (2020-09-30T20:00:11Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

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.