On Constant Distance Spacing Policies for Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) systems are considered as key potential enablers to improve driving safety and traffic efficiency. They allow for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time headway (CTH) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance headway (CDH) spacing policies provide superior potential to increase traffic capacity than CTH, a major drawback is a smaller safety margin at high velocities and string stability cannot be achieved using a one-vehicle look-ahead communication. The hypothesis of this work is to apply CDH only in few driving situations, when traffic throughput is of highest importance and safety requirements can be met due to comparably low velocities. As the most relevant situations where CDH could be applied, we identify starting platoons at signalized intersections. In this paper, we illustrate this idea. Specifically, we compare CTH with CDH regarding its potential to increase the capacity of traffic lights. Starting with the elementary situation of single traffic lights we expand our scope to whole traffic networks including several thousand vehicles in simulation. Using real world data to calibrate and validate vehicle dynamics simulation and traffic simulation, the study discusses the most relevant working parameters of CDH, CTH, and the traffic system in which both are applied.Comment: In preparation for submission to IEEE Transactions on Intelligent Transportation System

Driver Assistance for Safe and Comfortable On-Ramp Merging Using Environment Models Extended through V2X Communication and Role-Based Behavior Predictions

Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation information. Current research in the field of environment mapping is concerned with two major challenges. The first one is the integration of information from different sources e.g. on-board sensors like radar, camera, ultrasound and lidar, offline map data or backend information. The second challenge comprises in finding an abstract representation of this aggregated information with suitable interfaces for different driving functions and traffic situations. To overcome these challenges, an extended environment model is a reasonable choice. In this paper, we show that role-based motion predictions in combination with v2x-extended environment models are able to contribute to increased traffic safety and driving comfort. Thus, we combine the mentioned research areas and show possible improvements, using the example of a threading process at a motorway access road. Furthermore, it is shown that already an average v2x equipment penetration of 80% can lead to a significant improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance compared to non v2x-equipped vehicles during the threading process.Comment: the article has been accepted for publication during the 16th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP 2020), 8 pages, 8 figures, 1 tabl

A numerical study on constant spacing policies for starting platoons at oversaturated intersections

Cooperative Adaptive Cruise Control (CACC) is considered as a key potential enabler to improve driving safety and traffic efficiency. It allows for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time gap (CTG) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance gap (CDG) spacing policies provide superior potential to increase traffic capacity than CTG, their major drawbacks are a smaller safety margin at high velocities and that string stability cannot be achieved using a one-vehicle look-ahead communication. In this work, we propose to apply CDG only in a few driving situations, when traffic throughput is of highest importance and safety requirements can be met due to relatively low velocities. As the most relevant situations where CDG could be applied, we identify starting platoons at signalized intersections. With this application scenario we show that applying CDG only in a few specific and crucial situation can have a major impact on traffic efficiency. Specifically, we compare CTG with CDG regarding its potential to increase the capacity of traffic lights. Starting with the elementary situation of single traffic lights we expand our scope to whole traffic networks including several thousand vehicles in simulation. Using real world data to calibrate and validate vehicle dynamics simulation and traffic simulation, the study discusses the most relevant working parameters of CDG, CTG, and the traffic system in which both are applied.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

A Large-Scale SUMO-Based Emulation Platform

A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale, connected vehicle scenario. Various applications of the platform are presented

A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) were strong innovation drivers in recent years, towards the enhancement of traffic safety and efficiency. Today’s ADAS adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. However, to further enhance their benefit, ADAS need to cooperate in the future, using communication technologies. The resulting combination of vehicle automation and cooperation, for instance, enables solving hazardous situations by a coordinated safety intervention on multiple vehicles at the same point in time. Since the complexity of such cooperative ADAS grows with each vehicle involved, very large parameter spaces need to be regarded during their development, which necessitate novel development approaches. In this paper, we present an environment for rapidly prototyping cooperative ADAS based on vehicle simulation. Its underlying approach is either to bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. We reconcile the aspects of automation and cooperation in simulation by a tradeoff between precision and scalability. Reducing precise mapping of vehicle dynamics below the limits of driving dynamics enables simulating multiple vehicles at the same time. In order to validate this precision, we also present a method to validate the vehicle dynamics in simulation against real world vehicles

On performance estimation of prefetching algorithms for streaming content in automotive environments

Media streaming in automotive environments is becoming more important with the proliferation of 3G/4G technologies and the general demand for consuming internet content in cars. Especially the rising popularity of Music on Demand and Media Cloud Storage services pushes automotive manufactures efforts to provide decent music streaming capabilities in vehicles. This fact has recently brought car manufactures and music streaming services together. Thanks to today's mobile broad band Internet connectivity, music streaming is becoming available in the car. Volvo and Ford have announced to pair up with the popular music streaming service Spotify. Ford does already have a partnership with Rhapsody's music streaming and with the cloud music service Amazon Cloud Player while BMW is going to bring Rara to their vehicles

Evaluation of a new intelligent speed advisory system using hardware-in-the-loop simulation

In this paper we present a recently developed speed advisory system for ITS applications. A real vehicle embedded in a large scale SUMO simulation is used to demonstrate the efficacy of such a system

Forward-looking automated cooperative longitudinal control: Extending cooperative adaptive cruise control (CACC) with column-wide reach and automated network quality assessment

Cooperative automated Driver Assistance Systems (CoDAS) are a novel category of advanced driver assistance systems (ADAS), taking into account information received over wireless transmission from other vehicles and roadside infrastructure to enable or improve automated vehicle guidance and control. Research in cooperative longitudinal control so far focusses on improved distance control to a leading cooperative vehicle, often referred to as cooperative adaptive cruise control (CACC). In this work we expand on the concept of cooperative longitudinal control by introducing multi-object control planning, taking into account not only the leading vehicle, but also shared situational knowledge between vehicles and infrastructure. Thus, our system is able to track vehicles directly ahead either from direct cooperation or from transmitted knowledge and can adapt longitudinal control appropriately. We utilize the Collaborative Maneuver Protocol (CMP) to extend vehicle knowledge and estimate network quality. We have evaluated the function in various scenarios in the PHABMACS simulator and discuss effects of transmission quality on control parameters