A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles [PDF]
This paper proposes a novel hybrid car-following model: the physics-informed conditional generative adversarial network (PICGAN), designed to enhance multi-step car-following modeling in mixed traffic flow scenarios.
Lijing Ma +4 more
doaj +2 more sources
Robust Autonomous Intersection Control Approach for Connected Autonomous Vehicles [PDF]
Traffic light-free intersection control is envisioned to alleviate congestion and manage vehicles intelligently. With the help of vehicle-to-infrastructure (V2I) communication and edge computing (EC), vehicles are instructed to cross the intersection ...
Yuheng Zhang +4 more
doaj +2 more sources
Knowledge-guided self-learning control strategy for mixed vehicle platoons with delays [PDF]
As autonomous vehicles and traditional vehicles will coexist for several decades, how to efficiently manage the mixed traffic, while enhancing road throughput, fuel consumption and traffic stability becomes a challenge.
Jingyao Wang +7 more
doaj +2 more sources
Communicating with two vehicles immediately ahead boosts traffic capacity sixfold in connected autonomous vehicle platoons [PDF]
Addressing urban congestion through enhanced traffic capacity has emerged as a critical objective for connected autonomous driving technologies. An irredundant communication connectivity topology is essential for ensuring the high efficiency and ...
Shi-Teng Zheng +12 more
doaj +2 more sources
Resilient coordinated movement of connected autonomous vehicles [PDF]
In this paper, we consider coordinated movement of a network of vehicles consisting of a bounded number of malicious agents, that is, vehicles must reach consensus in longitudinal position and a common predefined velocity. The motions of vehicles are modeled by double-integrator dynamics and communications over the network are asynchronous with delays.
Mostafa Safi +2 more
openaire +2 more sources
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in ...
Sampo Kuutti +2 more
doaj +1 more source
The use of manipulators in space missions has become popular, as their applications can be extended to various space missions such as on-orbit servicing, assembly, and debris removal.
Mehran Raisi +2 more
doaj +1 more source
Probabilistic Meta-Conv1D Driving Energy Prediction for Mobile Robots in Unstructured Terrains
Driving energy consumption plays an important role in the navigation of autonomous mobile robots in off-road scenarios. However, the accuracy of the driving energy predictions is often affected by a high degree of uncertainty due to unknown and ...
Marco Visca +3 more
doaj +1 more source
Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains
This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties.
Marco Visca +3 more
doaj +1 more source
In Cooperative Intelligent Transportation Systems (C-ITS), vehicles can communicate with one another and with the infrastructure. The openness of the transportation system to new functionalities and use cases may create new vulnerabilities that must be ...
Farah Haidar +5 more
doaj +1 more source

