Poster Abstract: State Estimation and Sensor Fusion for Autonomous Driving in Mixed-Traffic Urban Environments

This study investigates the perceived safety of passengers while being on board of a driverless shuttle without a steward present. The aim of the study is to draw conclusions on factors that influence and contribute to perceived safety of passengers in driverless shuttles. For this, four different test rides were conducted, representing aspects that might challenge passengers’ perceived safety once driverless shuttles become part of public transport: passengers had to ride the shuttle on their own (without a steward present), had to interact with another passenger, and had to react to two different unexpected technical difficulties. Passengers were then asked what had influenced their perceived safety and what would contribute to it. Results show that perceived safety of passengers was high across all different test rides. The most important factors influencing the perceived safety of passengers were the shuttle’s driving style and passengers’ trust in the technology. The driving style was increasingly less important as the passengers gained experience with the driverless shuttle. Readily available contact with someone in a control room would significantly contribute to an increase in perceived safety while riding a driverless shuttle. For researchers, as well as technicians in the field of autonomous driving, our findings could inform the design and set-up of driverless shuttles in order to increase perceived safety; for example, how to signal passengers that there is always the possibility of contact to someone in a control room. Reacting to these concerns and challenges will further help to foster acceptance of AVs in society. Future research should explore our findings in an even more natural setting, e.g., a controlled mixed traffic environment.

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Demo: Attacking Multi-Sensor Fusion based Localization in High-Level Autonomous Driving

Proceedings Third International Workshop on Automotive and Autonomous Vehicle Security ◽

10.14722/autosec.2021.23010 ◽

2021 ◽

Author(s):

Junjie Shen ◽

Jun Yeon Won ◽

Zeyuan Chen ◽

Qi Alfred Chen

Keyword(s):

Sensor Fusion ◽

Autonomous Driving ◽

High Level

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Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

Remote Sensing ◽

10.3390/rs13224525 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4525

Author(s):

Junjie Zhang ◽

Kourosh Khoshelham ◽

Amir Khodabandeh

Keyword(s):

Autonomous Driving ◽

Measurement Model ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

High Definition ◽

Satellite Systems ◽

The Road ◽

Gnss Receivers ◽

Vehicle Positioning ◽

Global Navigation Satellite

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.

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Intelligent Multi Sensor Fusion System for Advanced Situation Awareness in Urban Environments

Communications in Computer and Information Science - Future Security ◽

10.1007/978-3-642-33161-9_17 ◽

2012 ◽

pp. 93-104 ◽

Cited By ~ 4

Author(s):

Georg Hummel ◽

Martin Russ ◽

Peter Stütz ◽

John Soldatos ◽

Lorenzo Rossi ◽

...

Keyword(s):

Sensor Fusion ◽

Situation Awareness ◽

Urban Environments ◽

Fusion System

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Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots

International Journal of Artificial Intelligence and Machine Learning ◽

10.4018/ijaiml.2020010101 ◽

2020 ◽

Vol 10 (1) ◽

pp. 1-17

Author(s):

Rufus Fraanje ◽

René Beltman ◽

Fidelis Theinert ◽

Michiel van Osch ◽

Teade Punter ◽

...

Keyword(s):

State Estimation ◽

Sensor Fusion ◽

Simulation Experiment ◽

Estimation Problem ◽

Unknown Input ◽

Fusion Algorithm ◽

Process Noise ◽

Distance Measurements ◽

Differential Drive ◽

Filter Solution

The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

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