Log In Sign Up

Autonomous Driving among Many Pedestrians: Models and Algorithms

by   Yuanfu Luo, et al.

Driving among a dense crowd of pedestrians is a major challenge for autonomous vehicles. This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.


PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

This paper presents a planning system for autonomous driving among many ...

The Pedestrian Patterns Dataset

We present the pedestrian patterns dataset for autonomous driving. The d...

Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles

Seamlessly operating an autonomous vehicle in a crowded pedestrian envir...

Single Shot Multitask Pedestrian Detection and Behavior Prediction

Detecting and predicting the behavior of pedestrians is extremely crucia...

What is the appropriate speed for an autonomous vehicle? Designing a Pedestrian Aware Contextual Speed Controller

Social acceptance is a major hurdle for autonomous vehicle technology, c...

Early warning of pedestrians and cyclists

State-of-the-art motor vehicles are able to break for pedestrians in an ...

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction

In applications such as autonomous driving, it is important to understan...