Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Abstract

Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants, and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that the vast amount of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method in order to guide the search towards regions with promising actions, yielding better results at lower computational costs.

Vehicular trajectory estimation utilizing slip angle based on GNSS Doppler/IMU

“…Thereafter, the effect of the slip angle correction on the trajectory was evaluated. Figure 18 shows the results of using the corrected slip angle estimation for the trajectory obtained from the conventional method [1,4] at the urban street shown in Fig. 8a.…”

“…Highly accurate vehicular trajectory estimation has become imminently important for automated vehicles and advanced driver assistance systems that have been developed in the recent years [1]. Accurate trajectory information has been used in various studies such as constraints for position estimation, 3-D mapping for automated vehicles, route planning, and vehicular control [2][3][4][5][6][7]. Accuracy is required for trajectory estimation of automated vehicles.…”

Vehicular Trajectory Estimation Utilizing Slip Angle Based on GNSS Doppler/IMU

“…Thereafter, the effect of the slip angle correction on 445 the trajectory was evaluated. Figure 17 shows the 446 results of using the corrected slip angle estimation for 447 the trajectory obtained from the conventional method 448 [1,4] at the urban street shown in Figure 8a…”

“…Using Equations (1)- (4), the vehicle trajectory can be 90 calculated from the output of the IMU using the wheel 91 speed. Therefore, the elements of trajectory that need to 92 be estimated are , β, ̇, SF, , and .…”

Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways

“…Therefore, researchers in the field of cooperative driving are working on maneuver planning algorithms that are able to interact with the surrounding traffic in a cooperative way. The published work in this field proves the functionality of their approaches based on a small number of scenarios, e.g., [1][2][3][4]. However, the main question is not whether a maneuver planner works in a single scenario, but how well it does in a wide range of realistic scenarios.…”