Related papers: CoLight: Learning Network-level Cooperation for Tr…
Intelligent streetlight systems divide the streetlight network into multiple sectors, activating only the streetlights in the corresponding sectors when traffic elements pass by, rather than all streetlights, effectively reducing energy…
Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this…
We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the length of cycle time period of traffic lights at each signal is autonomously adapted. We find a self-organizing collective…
We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is…
Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving policy. Current research rarely considers the…
This paper proposes a simplified version of classical models for urban transportation networks, and studies the problem of controlling intersections with the goal of optimizing network-wide congestion. Differently from traditional…
This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed…
Traffic Congestions and accidents are major concerns in today's transportation systems. This thesis investigates how to optimize traffic flow on highways, in particular for merging situations such as intersections where a ramp leads onto…
Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior performance over…
One of the main challenges in managing traffic at multilane intersections is ensuring smooth coordination between human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). This paper presents a novel traffic signal control…
Traffic light control is important for reducing congestion in urban mobility systems. This paper proposes a real-time traffic light control method using deep Q learning. Our approach incorporates a reward function considering queue lengths,…
Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where…
Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years. However, existing cooperative driving strategies…
Urban traffic congestion, particularly at intersections, significantly affects travel time, fuel consumption, and emissions. Traditional fixed-time signal control systems often lack the adaptability to effectively manage dynamic traffic…
This work examines the implications of uncoupled intersections with local real-world topology and sensor setup on traffic light control approaches. Control approaches are evaluated with respect to: Traffic flow, fuel consumption and noise…
Applying reinforcement learning to autonomous driving entails particular challenges, primarily due to dynamically changing traffic flows. To address such challenges, it is necessary to quickly determine response strategies to the changing…
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an…
Foundation models have shown great promise in various fields of study. A potential application of such models is in computer network traffic analysis, where these models can grasp the complexities of network traffic dynamics and adapt to…
Recent graph-based models for multi-intent SLU have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling. However, existing methods (1) only model the unidirectional…
In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, cooperative driving is a promising first step in automated…