DeepSignals: Predicting Intent of Drivers Through Visual Signals

2019-05-03 · Davi Frossard, Eric Kee, Raquel Urtasun

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.

📄 PDF Abstract BibTeX arXiv:1905.01333

Code (0)

등록된 구현이 없습니다.

Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention

2022-07-02 · Hongde Wu, Mingming Liu

Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful…

MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

2019-12-17 · ICCV 2021 10 · Sonia Baee, Erfan Pakdamanian, Inki Kim, Lu Feng 외

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accid…

Autonomous Vehicles reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Prediction of Lane Change Intentions of Human Drivers using an LSTM, a CNN and a Transformer

2025-07-11 · Francesco De Cristofaro, Felix Hofbaur, Aixi Yang, Arno Eichberger arxiv

Lane changes of preceding vehicles have a great impact on the motion planning of automated vehicles especially in complex traffic situations. Predicting them would benefit the public in terms of safety and efficiency. Wh…

Motion Planning

Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks

2018-07-26 · Alex Zyner, Stewart Worrall, Eduardo Nebot

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicentre of highly variable vehicle movement an…

Autonomous Vehicles Clustering Trajectory Prediction

Learning and Inferring a Driver's Braking Action in Car-Following Scenarios

2018-01-11 · Wenshuo Wang, Junqiang Xi, Ding Zhao

Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's intent to brake in car-following scenari…

Sensitivity Specificity