Video action recognition for lane-change classification and prediction of surrounding vehicles
In highway scenarios, an alert human driver will typically anticipate early cut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly. Autonomous vehicles must anticipate these situations at an early stage too, to increase their safety and efficiency. In this work, lane-change recognition and prediction tasks are posed as video action recognition problems. Up to four different two-stream-based approaches, that have been successfully applied to address human action recognition, are adapted here by stacking visual cues from forward-looking video cameras to recognize and anticipate lane-changes of target vehicles. We study the influence of context and observation horizons on performance, and different prediction horizons are analyzed. The different models are trained and evaluated using the PREVENTION dataset. The obtained results clearly demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles proving an accuracy higher than 90% in time horizons of between 1-2 seconds.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionAutonomous VehiclesGeneral ClassificationTemporal Action LocalizationSimilar Papers 제목 키워드 기반
Lane Change Classification and Prediction with Action Recognition Networks
Anticipating lane change intentions of surrounding vehicles is crucial for efficient and safe driving decision making in an autonomous driving system. Previous works often adopt physical variables such as driving speed, …
Action RecognitionAutonomous DrivingClassificationDecision MakingRisky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipeline, limiting th…
Action RecognitionGeneral ClassificationInstance SegmentationSemantic SegmentationTwo-Stream Networks for Lane-Change Prediction of Surrounding Vehicles
In highway scenarios, an alert human driver will typically anticipate early cut-in and cut-out maneuvers of surrounding vehicles using only visual cues. An automated system must anticipate these situations at an early st…
Action RecognitionPredictionTemporal Action LocalizationVocal Bursts Valence PredictionPhase Space Reconstruction Network for Lane Intrusion Action Recognition
In a complex road traffic scene, illegal lane intrusion of pedestrians or cyclists constitutes one of the main safety challenges in autonomous driving application. In this paper, we propose a novel object-level phase spa…
Action RecognitionAutonomous DrivingObjectObject Tracking+4Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition Networks
Recognizing distractions on the road is crucial to reduce traffic accidents. Video-based networks are typically used, but are limited by their computational cost and are vulnerable to viewpoint changes. In this paper, we…
Action ClassificationData AugmentationSkeleton Based Action Recognitionspeech-recognition+1