FIGURE 4 - uploaded by Georgios Grigoropoulos
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The visualisation of the sensor working principle including the projected trajectory. The thin red lines represent the LiDAR's recognition rays, with the thick red and green lines showing the derived left and right lane border and, ultimately, the proposed driving path given by the green chevrons.
Source publication
With the undeniable advance of automated vehicles and their gradual integration in day-today urban traffic, many new technologies have been developed that offer great potential for this emerging field of research. However, testing automated vehicle technologies in real road traffic with vulnerable road users (VRUs) is still a complicated and time c...
Contexts in source publication
Context 1
... high frequency of the sensor updates (given by the time step in SUMO, or, if set, the sensor's frequency) ensures sufficient input for the path planning process, even though the evaluation logic skips some data points. Figure 4 shows the described logic showing color-coded outputs. The Camera Sensor is modeled as a cone represented by a polygon, basically determining the agent's field of view. ...
Context 2
... camera's specifications assume the ability to recognize nearby intersections, as well as the position, speed, and orientation at the current time step of nearby agents falling inside the cone of view. The sensor layout is presented in Figure 3 and a detailed example of the sensor setup in operation in Figure 4. ...
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Citations
... This is especially relevant when considering active road users, due to the difficulty of replicating interaction dynamics in busy scenarios with group behavior . Such methods focused on different road types, including intersections (Rampf et al., 2023) and roundabouts , with promising results in terms of modeling accuracy. The continuous development and refinement of simulation studies leads to significant benefits in the field of AV safety, including the potential for reliable AV testing in urban scenarios. ...
The inevitable impact of autonomous vehicles (AV) on traffic safety is becoming a reality with the progressive deployment of these vehicles in different parts of the world. Still, many questions linger in the minds of road users that will share the road and interact with these AVs on a daily basis. To answer some of these questions, this study utilized recently collected real-world AV data from the United States, with the focus mainly targeting active road users. Specifically, the 1,492 h of recorded trips were processed to extract AV-pedestrian and AV-cyclist interactions of different movement types. The interactions were then investigated to gain a better understanding of the active road users' behavior, while excluding any interactions that involved intervention from the AVs' human test drivers. Through deep maximum entropy inverse reinforcement learning (DME-IRL), the reward functions describing the utility of active road users were retrieved and assessed for five interaction scenarios, including parallel, opposing, crossing, and turning (left and right) interactions. In addition, the policies developed as part of the solution were used to simulate the behavior of active road users and validate the resulting conflicts in terms of safety and evasive actions. Overall, the utilized approach demonstrated high accuracy in mimicking the interaction behavior of active road users when encountering an AV, with 81-84% accuracy in predicting evasive actions in parallel and opposing interactions and 12-17% mean absolute error for safety indicators in crossing and turning interactions. In addition, the resulting reward functions provided reliable insight onto the preferences and considerations of the active road users in these situations. Overall, cyclists tend to be less cautious around AVs compared to pedestrians, while the AVs tend to slow down and leave sufficient distance from all active road users in most cases. Still, the robotic behavior of the AVs, which can sometimes be inconsistent, leads to risky evasive actions by the active road users, which can affect other road users in busy intersections.
