Abstract
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.
Highlights
One of the main challenges towards highly automated driving is the comprehensive environmental perception of vehicles
The main contributions of this work are threefold: (i) we present a short survey of network- and perception-related evaluation metrics applied to collective perception in the literature, (ii) we define new metrics that quantify the quality of a vehicle environmental model from a more safety-oriented perspective, and (iii) we present simulation results that show the improvement in the vehicle environmental model due to collective perception based on the presented network, perception, and safety-related metrics
Two highway segments with different traffic densities were investigated with the help of TEPLITS, a test and simulation environment for intelligent transportation systems developed in the scope of the public funded project IMAGinE
Summary
One of the main challenges towards highly automated driving is the comprehensive environmental perception of vehicles. V2X communication has emerged as a promising technology to mitigate this lack, allowing traffic participants to share all kinds of information to improve their environmental perception and strengthen their decision-making basis [1]. Examples of V2X applications are cooperative awareness and collective (or cooperative) perception, allowing vehicles to share data about their state and detected objects in the surroundings, respectively. Collective perception gives traffic participants the possibility to exchange information about objects in their Local Environmental Models (LEMs). Information received using V2X communication is fused into the so-called Global Environmental Model (GEM), which is managed separately from the LEM. We developed analytical models to evaluate the performance of the collective perception service in IEEE 802.11p [6] and C-V2C mode 4 [7] networks in terms of its impact on the traffic participant’s gained environmental perception. The main contributions of this work are threefold: (i) we present a short survey of network- and perception-related evaluation metrics applied to collective perception in the literature, (ii) we define new metrics that quantify the quality of a vehicle environmental model from a more safety-oriented perspective, and (iii) we present simulation results that show the improvement in the vehicle environmental model due to collective perception based on the presented network-, perception-, and safety-related metrics
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