The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?

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 anticipat…

Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

Detecting 3D lanes from the camera is a rising problem for autonomous vehicles. In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view. With this transformation, we can get rid of the perspective effects so that 3D lanes would look similar and can accurately be fitted by low-order polynomials. However, mainstream 3D lane detectors rely on perfect camera poses provided by other sensors, which is expensive and encounters multi-sensor calibration issues. To overcome this problem, we propose to predict 3D la…

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is critical for the safe and comfortable operation of automated driving systems. The majority of previous studies rely on detecting a manoeuvre that has been already started, rather than predicting the manoeuvre in advance. Furthermore, most of the previous works do not estimate the key timings of the manoeuvre (e.g., crossing time), which can actually yield more useful information for the decision making in the ego vehicle…

Goal-Directed Occupancy Prediction for Lane-Following Actors

Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road ac…

LMT-Net：用于从稀疏车辆观测自动生成高清地图的车道模型Transformer网络

LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations

针对高清地图生成依赖人工标注、扩展性差的问题，提出LMT-Net，利用稀疏车辆观测自动预测车道对及连通性，在内部数据集上优于基线，支持高速公路和非高速公路场景。

Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks

Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. This study proposes a method to predict potentially dangerous cut-in maneuvers happening in the ego lane. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera, and we classify the target vehicle's maneuver based on the recent video frames. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step. It is more computationally efficient than other vision-based methods since …

BodyMAP——联合预测卧床人员的人体网格与3D施加压力图

BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed

本文提出BodyMAP框架，融合深度图像与2D压力床垫图像，联合预测卧床者全身人体网格及逐顶点3D压力图，并以弱监督形式BodyMAP-WS实现隐式3D压力学习，两项任务均较SOTA提升25%。

基于机器学习的自动驾驶车辆意图轨迹识别与预测

Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving

本文针对自动驾驶变道安全问题，提出一种基于深度学习的变道行为预测方法，旨在准确识别驾驶员变道意图，促进安全变道并提升道路安全性。

视频取代高精地图：直接从航拍图像预测多智能体行为

Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images

本文提出一种基于航拍图像的地图表示AIM，以极低标注成本替代高精地图，并将其集成到可微驾驶仿真器中，在多智能体轨迹预测任务上取得与栅格化高精地图相当的性能，尤其对行人预测表现突出。

Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning

This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicle positions, motion histories, road configuration, and vehicle interactions are easily included in the prediction model using basic visual representations. The U-net model has been selected as the prediction kernel to generate future visual representations of the scene using an image-to-image regression approach. A method has been implemented to extract vehicle positions from the generated graphical representations t…

Maneuver-Aware Pooling for Vehicle Trajectory Prediction

Autonomous vehicles should be able to predict the future states of its environment and respond appropriately. Specifically, predicting the behavior of surrounding human drivers is vital for such platforms to share the same road with humans. Behavior of each of the surrounding vehicles is governed by the motion of its neighbor vehicles. This paper focuses on predicting the behavior of the surrounding vehicles of an autonomous vehicle on highways. We are motivated by improving the prediction accuracy when a surrounding vehicle performs lane change and highway merging maneuvers. We propose a nove…