Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios

• URL: http://arxiv.org/abs/2405.05354v1
• Date: Wed, 8 May 2024 18:33:08 GMT
• Title: Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
• Authors: Chirag Parikh, Ravi Shankar Mishra, Rohan Chandra, Ravi Kiran Sarvadevabhatla,
• Abstract summary: Existing video recognition approaches work well for common behaviors. But the performance is sub-par for underrepresented/rare behaviors typically found in tail of the behavior class distribution. We propose Transfer-LMR, a modular training routine for improving the recognition performance across all driving behavior classes.
• Score: 11.431703211595563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation. Existing video recognition approaches work well for common behaviors (e.g. "drive straight", "brake", "turn left/right"). However, the performance is sub-par for underrepresented/rare behaviors typically found in tail of the behavior class distribution. To address this shortcoming, we propose Transfer-LMR, a modular training routine for improving the recognition performance across all driving behavior classes. We extensively evaluate our approach on METEOR and HDD datasets that contain rich yet heavy-tailed distribution of driving behaviors and span diverse traffic scenarios. The experimental results demonstrate the efficacy of our approach, especially for recognizing underrepresented/rare driving behaviors.

Related papers

• Situation Awareness for Driver-Centric Driving Style Adaptation [3.568617847600189]
  We propose a situation-aware driving style model based on different visual feature encoders pretrained on fleet data. Our experiments show that the proposed method outperforms static driving styles significantly and forms plausible situation clusters.
  arXiv  Detail & Related papers  (2024-03-28T17:19:16Z)
• 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)
• Identification of Driving Heterogeneity using Action-chains [3.596647660010906]
  This study introduces a comprehensive framework for identifying driving heterogeneity from an Action-chain perspective. A rule-based segmentation technique that considers the physical meanings of driving behaviour is proposed. Next, an Action phase Library including descriptions of various driving behaviour patterns is created based on the segmentation findings.
  arXiv  Detail & Related papers  (2023-07-31T17:04:39Z)
• Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
  Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
  arXiv  Detail & Related papers  (2023-04-23T16:01:36Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [75.83518507463226]
  Nonobjective driving experience is difficult to model. In this paper, we propose a FeedBack Loop Network (FBLNet) which attempts to model the driving experience accumulation procedure. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Transferable and Adaptable Driving Behavior Prediction [34.606012573285554]
  We propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts from the INTERACTION dataset.
  arXiv  Detail & Related papers  (2022-02-10T16:46:24Z)
• Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning [3.4979173592795374]
  Drivers have unique and rich driving behaviors when operating vehicles in traffic. This paper presents a novel driver behavior learning approach that captures the uniqueness and richness of human driver behavior in realistic driving scenarios.
  arXiv  Detail & Related papers  (2021-07-01T20:18:03Z)
• Learning to drive from a world on rails [78.28647825246472]
  We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. Our method ranks first on the CARLA leaderboard, attaining a 25% higher driving score while using 40 times less data.
  arXiv  Detail & Related papers  (2021-05-03T05:55:30Z)
• Learning Accurate and Human-Like Driving using Semantic Maps and Attention [152.48143666881418]
  This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. We exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data.
  arXiv  Detail & Related papers  (2020-07-10T22:25:27Z)
• Modeling Human Driving Behavior through Generative Adversarial Imitation Learning [7.387855463533219]
  This article describes the use of Generative Adversarial Imitation Learning (GAIL) for learning-based driver modeling. Because driver modeling is inherently a multi-agent problem, this paper describes a parameter-sharing extension of GAIL called PS-GAIL to tackle multi-agent driver modeling. This paper describes Reward Augmented Imitation Learning (RAIL), which modifies the reward signal to provide domain-specific knowledge to the agent.
  arXiv  Detail & Related papers  (2020-06-10T05:47:39Z)

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.