Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

Cooperative driving is a promising paradigm to improve traffic efficiency and safety. In congested traffic scenarios, such cooperation allows for safe maneuvering and driving with small inter-vehicle spatial gaps. The vehicles involved coordinate their movements in real-time and continuously update each other about their maneuver execution status by means of Vehicle-to-Everything (V2X) communication. However, unreliable V2X communication increases the Age of Information (AoI) of vehicles’ status updates, posing a challenge in situations where emergency braking is required during cooperative maneuvering. To address the interplay between unreliable V2X communication and the resulting impact on traffic safety, we introduce a so-called safety time function , specifically designed for cooperative driving use-cases. The safety time function provides the time available for a vehicle to react to an unexpected event of another vehicle – such as emergency braking to avoid a collision. We provide a computationally efficient algorithm for the computation of safety time functions, which allows for efficient and safe cooperative maneuver planning – even in dense traffic scenarios with many vehicles involved. We show the applicability of our proposed safety time function based on the assessed communication quality for IEEE 802.11p-based V2X communication to meet safety constraints in dense vehicular traffic.

One of the major challenges on the way towards full traffic automation is the incomplete environmental awareness of the traffic participants. The most promising approach to overcome the physical performance limitations due to the restricted range of current vehicle on-board sensors such as cameras, LIDARs and radars is to extend the vehicle's perception using vehicle-to-everything (V2X) communication. It allows traffic participants to share information gathered by their sensors among each other. This application of V2X-communication is commonly referred to as collective perception and will be mainly supported in Europe by the collective perception service currently under standardization at the European Telecommunications Standards Institute (ETSI). This paper is the first to analytically evaluate the performance of the service when operated using the ad hoc mode of cellular V2X, known as Mode 4, as defined by the 3GPP in Release 14 and aims at supporting the ongoing standardization efforts. To this end, several performance metrics are investigated on the example of a highway scenario under varying conditions and the usability of the LTE-V based service for two vehicle safety applications is discussed.

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The simulation of traffic scenes in the environment of an automated vehicle promises to make significant contribution to the validation of automated driving. The construction of models which describe traffic scenes in a generic manner is complicated, since the parameter space of the scenes is infinite. This paper introduces a statistical approach to generate traffic scenes in intersections. A generic model which allows us to represent the scenes is proposed. The concept of Bayesian networks is used to fit the model onto a publicly accessible dataset and to infer traffic scenes from the model. A quantitative evaluation of the results is achieved by the calculation of the total variation distance (TVD) between the distributions of several physical properties.

The number and complexity of newly developed automated driving systems has been constantly rising over the past decade. Especially the introduction of vehicle-to-everything (V2X) communication is expected to further potentiate this development. In order to be deployed, the functional safety of the developed systems has to be assured previously. However, the testing in a representative number of field tests is costly and time-consuming. For this reason, virtual test drives have risen as an important option for design and testing of automated driving technologies, leaving only the final validation to test with real vehicles and thus significantly reducing the overall expenditure. The authors of the work at hand introduce a simulation framework based on the vehicle simulator CarMaker, complemented with the middleware platform Robot Operating System (ROS) and fed with real traffic data, which allows to automatically test advanced driver assistance systems for a large number of real world scenarios by varying topology, vehicle and communication parameters, among others. The simulation framework is then used to demonstrate the benefit of collective perception (i.e. sharing of on-board sensor data among nearby vehicles by V2X communication) for a vehicle merging into a freeway, with metrics such as the vehicle awareness on spot and the time it has to plan and execute its maneuver.