Motion planners for autonomous driving improve traffic safety through collision-free motion generation along the path. However, conventional motion planners render passengers uncomfortable as a result of jerky motion. To overcome this, we propose a model predictive control (MPC) based motion planner that not only ensures safety but also improves driving comfort. The proposed planner generates path tracking and collision-free maneuvers to ensure safety, and improve driving comfort by minimizing acceleration and jerk. Collision-free maneuvers include vehicle following and overtaking. The target speed is determined by comprehensively considering path tracking performance improvement and whether overtaking is possible. The speed of the vehicle is controlled by considering longitudinal acceleration and jerk minimization. The steering command is determined by considering both path tracking error reduction, and lateral acceleration and jerk minimization. In cases where vehicle overtaking is required and during high-speed driving conditions, the consideration of lateral acceleration and jerk minimization increases to improve driving comfort. The proposed planner is formulated as a convex optimization problem. The effectiveness of the proposed planner was evaluated in path tracking and collision avoidance simulations. The simulation results confirm that the proposed planner ensures vehicle safety through lane keeping and collision avoidance, and improves driving comfort.
Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.
The paths generated by sampling-based path planning are generally not smooth and often generate multiple unnecessary robot posture changes in the task space. To mitigate such issues with a planned path from sampling-based path planners, shortcut-based path shortening algorithms are commonly adopted in the field of robot manipulator path planning as a post-processing step. In this paper, we analyze shortcut-based algorithms and propose a new approach based on the idea of parallelism for faster path shortening so that it can be more applicable in environments where a path has to be generated as quickly as possible to avoid collisions with other moving objects around the manipulator. Through performance comparisons in simulations, it is shown that the proposed approach can obtain a well-shortened as well as much smooth path compared to the original path faster than conventional shortcut-based algorithms and an optimization-based approach developed for collision-free path generation.
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