Update Delay: A new Information-Centric Metric for a Combined Communication and Application Level Reliability Evaluation of CAM based Safety Applications

Standard network metrics, such as throughput, latency and reception probability, are the most popular performance indicators used in the literature to describe and compare communication protocol variations. However, these “traditional” network-centric PI are not adapted to the distributed, information-centric nature of the beaconing communication pattern, nor do they cover application level reliability or freshness of information. In this paper, we introduce a more suitable metric called Update Delay, represented as a Complementary Cumulative Distribution Function (CCDF). We will show how this single Update Delay performance indicator can be an optimal representation of the freshness and reliability of the information about a certain transmitter, i.e. awareness about vehicles and their current state in the vicinity. This paper extends on the methodological aspects of the approach, as well as introduces several concrete examples

A RoundD-like Roundabout Scenario in CARLA Simulator

Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions

Modeling and Analysis of Mixed Flow of Cars and Powered Two Wheelers

International audienceIn modern cities, a rapid increase of motorcycles and other types of Powered Two-Wheelers (PTWs) is observed as an answer to long commuting in traffic jams and complex urban navigation. Such increasing penetration rate of PTWs creates mixed traffic flow conditions with unique characteristics that are not well understood at present. Our objective is to develop an analytical traffic flow model that reflects the mutual impacts of PTWs and Cars. Unlike cars, PTWs filter between cars, have unique dynamics, and do not respect lane discipline, therefore requiring a different modeling approach than traditional " Passenger Car Equivalent " or " Follow the Leader ". Instead, this work follows an approach that models the flow of PTWs similarly to a fluid in a porous medium, where PTWs " percolate " between cars depending on the gap between them. Our contributions are as follows: (I) a characterization of the distribution of the spacing between vehicles by the densities of PTWs and cars; (II) a definition of the equilibrium speed of each class as a function of the densities of PTWs and cars; (III) a mathematical analysis of the model's properties (IV) an impact analysis of the gradual penetration of PTWs on cars and on heterogeneous traffic flow characteristics

Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator

International audienceAt early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proof-of-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them

COLOMBO Deliverable 1.1: Scenario Specifications and Required Modifications to Simulation Tools

While targeting on supporting descriptions of scenarios and extensions to the simulation suite, the document additionally delivers a complete overview of the evaluation procedures to use in COLOMBO. Starting with an overview of the evaluation process, based on work done in the FESTA project, the document includes definitions of the performance indicators to use. These were originally produced by the iTETRIS project (by consortium partners of COLOMBO, mainly) and was extended within COLOMBO by performance indicators that describe the behaviour of inter-vehicle communication. To put the work on a scientific ground, a performed comparison of 40 scientific simulation studies is given, that shows that no standard scenarios and metrics exist. Additionally the document lists feature extensions which shall be implemented into the simulation tools within the COLOMBO project. Applicable software and data yielding to the scenarios were provided to the COLOMBO partners. As targeted, the document lists the scenarios made available within COLOMBO, distinguishing synthetic and real-world scenarios. Overall, seven scenarios based on real-world data were made available. Additionally, a tool that allows generating a large variety of synthetic scenarios is presented. The document ends with an extension (against the one given in D5.1) of requirements put on the simulations suite