Prioritizing Cyclists at Signalized Intersections Using Observations from Connected Autonomous Vehicles

When making trips in urban environments, cyclists lose time as they stop and idle at signalized intersections. The main objective of this study was to show how augmenting the situational awareness of traffic signal controllers, using observations from moving sensor platforms, can enable prioritization of cyclists and reduce lost time within...

Framework for Network-Constrained Tracking of Cyclists and Pedestrians

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks...

AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities

We present a design for a novel mobile sensing system (AMSense) that uses vehicles as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians and cyclists (active modes) in urban environments. In this dynamic, multi-sensor approach, real-time data, algorithms, and models are fused to estimate presence, positions and...

Urban Mobility Observatory

The Urban Mobility Observatory (UMO) will gather, store, and disseminate empirical multi-modal traffic, transport and mobility data, using a well-balanced set of innovative data collection methods. It will make these comprehensive data available for scientific research to develop and test new theories and models to better understand, predict and...

Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves

The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control...

Optimal lane change times and accelerations of autonomous and connected vehicles

This contribution puts forward a flexible approach to model the decision-making or design controller for automated driving systems, where tactical-level lane change decisions and control-level accelerations are jointly evaluated based on iteratively solving an online optimization problem. The key idea is that automated vehicles determine lane...

Delay-compensating strategy to enhance string stability of adaptive cruise controlled vehicles

A novel strategy to enhance string stability of autonomous vehicles with sensor delay and actuator lag is proposed based on a model predictive control framework. To compensate sensor delay, the approach entails estimating the (unknown) system state at the current time using the system state in a previous time, the applied control history and a...

Modelling Driver Assitance Systems by Optimal Control

Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper put forward a receding horizon control framework to model driver assistance systems. The accelerations of automated vehicles are determined to optimise a cost function,...