Applications of deep learning in intelligent transportation systems
AK Haghighat, V Ravichandra-Mouli… - Journal of Big Data …, 2020 - Springer
Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and
faster development by implementing deep learning techniques in problem domains which …
faster development by implementing deep learning techniques in problem domains which …
Baffle: Blockchain based aggregator free federated learning
P Ramanan, K Nakayama - 2020 IEEE international conference …, 2020 - ieeexplore.ieee.org
A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to
maintain and update the global model. However, in many cases orchestrating a centralized …
maintain and update the global model. However, in many cases orchestrating a centralized …
Deep -Network-Based Route Scheduling for TNC Vehicles With Passengers' Location Differential Privacy
The transportation network company (TNC) services efficiently pair the passengers with the
vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services …
vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services …
Using reinforcement learning to minimize taxi idle times
K O'Keeffe, S Anklesaria, P Santi… - Journal of Intelligent …, 2022 - Taylor & Francis
Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis
should follow in order to minimize their idle times are hard to calculate; they depend on …
should follow in order to minimize their idle times are hard to calculate; they depend on …
Intelligent cruise guidance and vehicle resource management with deep reinforcement learning
The emergence of new business and technological models for urban-related transportation
has revealed the need for transportation network companies (TNCs). Most research works …
has revealed the need for transportation network companies (TNCs). Most research works …
Deep reinforcement learning based strategy for quadrotor UAV pursuer and evader problem
In recent years, there have occurred many incidents that unmanned aerial vehicles (UAVs)
in the field of national security. While in some situations, UAVs may be deployed …
in the field of national security. While in some situations, UAVs may be deployed …
Dynamic Pricing for Vehicle Dispatching in Mobility-as-a-Service Market via Multi-Agent Deep Reinforcement Learning
G Sun, GO Boateng, K Liu… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Vehicle dispatching in the mobility-as-a-service (MaaS) market has gradually become a
situation of multi-service provider competition and coexistence. However, most existing …
situation of multi-service provider competition and coexistence. However, most existing …
No one left behind: Avoid hot car deaths via WiFi detection
According to the safety organization Kids and Cars, in US, an average of 38 children die
each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm …
each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm …
Cyber-physical risk driven routing planning with deep reinforcement-learning in smart grid communication networks
In modern grid systems which is a typical cyber-physical System (CPS), information space
and physical space are closely related. Once the communication link is interrupted, it will …
and physical space are closely related. Once the communication link is interrupted, it will …
Accelerating Experience Replay for Deep Q-Networks with Reduced Target Computation
B Zigon, F Song - 2023 - scholarworks.iupui.edu
Mnih's seminal deep reinforcement learning paper that applied a Deep Q-network to Atari
video games demonstrated the importance of a replay buffer and a target network. Though …
video games demonstrated the importance of a replay buffer and a target network. Though …