Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs

2018-05-15 · Nachiket Deo, Mohan M. Trivedi

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles. Multiple interacting agents, the multi-modal nature of driver behavior, and the inherent uncertainty involved in the task make motion prediction of surrounding vehicles a challenging problem. In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. Our model assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. Our results show an improvement in terms of RMS values of prediction error. We also present an ablative analysis of the components of our proposed model and analyze the predictions made by the model in complex traffic scenarios.

📄 PDF Abstract BibTeX arXiv:1805.05499

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous Vehiclesmotion predictionNavigatePrediction Trajectory Prediction

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

2022-12-22 · Jian Zhou, Björn Olofsson, Erik Frisk

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting…

Autonomous Vehicles Collision Avoidance Model Predictive Control Motion Planning+1

Class-Aware Attention for Multimodal Trajectory Prediction

2022-08-31 · Bimsara Pathiraja, Shehan Munasinghe, Malshan Ranawella, Maleesha De Silva 외

Predicting the possible future trajectories of the surrounding dynamic agents is an essential requirement in autonomous driving. These trajectories mainly depend on the surrounding static environment, as well as the past…

Autonomous Driving Prediction Trajectory Prediction

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle

2025-09-01 · Ziteng Gao, Jiaqi Qu, Chaoyu Chen arxiv

Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change d…

Reinforcement Learning Trajectory Prediction Autonomous Vehicles

MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration

2024-07-31 · Xi Chen, Rahul Bhadani, Zhanbo Sun, Larry Head

The prediction of surrounding vehicle trajectories is crucial for collision-free path planning. In this study, we focus on a scenario where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing …

Autonomous Vehicles Data Integration Prediction Trajectory Prediction

DsMCL: Dual-Level Stochastic Multiple Choice Learning for Multi-Modal Trajectory Prediction

2020-03-19 · Zehan Wang, Sihong Zhou, Yuyao Huang, Wei Tian

For both driving safety and efficiency, automated vehicles should be able to predict the behavior of surrounding traffic participants in a complex dynamic environment. To accomplish such a task, trajectory prediction is …

Multiple-choice Prediction Trajectory Prediction