Development and Experimentation of an Autonomous Vehicle Platoon for Urban Environments

2010 ◽  
Author(s):  
M. Parent ◽  
P. Petrov ◽  
C. Boussard
Keyword(s):  
Autonomous Vehicle ◽  
Urban Environments ◽  
Vehicle Platoon

scholarly journals Safety Assessment and Simulation of Autonomous Vehicle in Urban Environments

2021 ◽  
Vol 1140 (1) ◽  
pp. 012032
Author(s):  
Mohsen Malayjerdi ◽  
Bariş Cem Baykara ◽  
Raivo Sell ◽  
Ehsan Malayjerdi
Keyword(s):  
Safety Assessment ◽  
Autonomous Vehicle ◽  
Urban Environments

Robust Approximate Constraint-Following Control for Autonomous Vehicle Platoon Systems

2017 ◽  
Vol 20 (4) ◽  
pp. 1611-1623 ◽  
Author(s):  
Xiaomin Zhao ◽  
Y. H. Chen ◽  
Han Zhao
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Platoon

Deep tracking in the wild: End-to-end tracking using recurrent neural networks

2017 ◽  
Vol 37 (4-5) ◽  
pp. 492-512 ◽  
Author(s):  
Julie Dequaire ◽  
Peter Ondrúška ◽  
Dushyant Rao ◽  
Dominic Wang ◽  
Ingmar Posner
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicle ◽  
Urban Environments ◽  
Positive Contribution ◽  
Efficient Manner ◽  
Model Free ◽  
Novel Approach ◽  
In The Wild ◽  
End To End ◽  
Traditional Approaches

This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban environments. Whereas traditional approaches for tracking often feature numerous hand-engineered stages, this method is learned end-to-end and can directly predict a fully unoccluded occupancy grid from raw laser input. We employ a recurrent neural network to capture the state and evolution of the environment, and train the model in an entirely unsupervised manner. In doing so, our use case compares to model-free, multi-object tracking although we do not explicitly perform the underlying data-association process. Further, we demonstrate that the underlying representation learned for the tracking task can be leveraged via inductive transfer to train an object detector in a data efficient manner. We motivate a number of architectural features and show the positive contribution of dilated convolutions, dynamic and static memory units to the task of tracking and classifying complex dynamic scenes through full occlusion. Our experimental results illustrate the ability of the model to track cars, buses, pedestrians, and cyclists from both moving and stationary platforms. Further, we compare and contrast the approach with a more traditional model-free multi-object tracking pipeline, demonstrating that it can more accurately predict future states of objects from current inputs.

The Value of Autonomous Vehicles for Last-Mile Deliveries in Urban Environments

2021 ◽  
Author(s):  
Sara Reed ◽  
Ann Melissa Campbell ◽  
Barrett W. Thomas
Keyword(s):  
Operations Management ◽  
Autonomous Vehicles ◽  
Completion Time ◽  
Autonomous Vehicle ◽  
Optimal Solution ◽  
Fixed Time ◽  
Urban Environments ◽  
Assisted Delivery ◽  
Autonomous Case ◽  
Number Of Customers

We demonstrate that autonomous-assisted delivery can yield significant improvements relative to today’s system in which a delivery person must park the vehicle before delivering packages. We model an autonomous vehicle that can drop off the delivery person at selected points in the city where the delivery person makes deliveries to the final addresses on foot. Then, the vehicle picks up the delivery person and travels to the next reloading point. In this way, the delivery person would never need to look for parking or walk back to a parking place. Based on the number of customers, driving speed of the vehicle, walking speed of the delivery person, and the time for loading packages, we characterize the optimal solution to the autonomous case on a solid rectangular grid of customers, showing the optimal solution can be found in polynomial time. To benchmark the completion time of the autonomous case, we introduce a traditional model for package delivery services that includes the time to search for parking. If the time to find parking is ignored, we show the introduction of an autonomous vehicle reduces the completion time of delivery to all customers by 0%–33%. When nonzero times to find parking are considered, the delivery person saves 30%–77% with higher values achieved for longer parking times, smaller capacities, and lower fixed time for loading packages. This paper was accepted by Vishal Gaur, operations management.

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