It's critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space where the regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition. In order to identify all sizes of objects precisely, we combine the features of the last three layers of the CNN to extract multi-scale features of the ROIs. The evaluation results on the KITTI dataset demonstrate that : (1) Unlike sliding windows that produce thousands of candidate object-region proposals, 3D LIDAR provides an average of 86 real candidates per frame and the minimal recall rate is higher than 95%, which greatly lowers the proposals extraction time; (2) The average processing time for each frame of the proposed method is only 66.79ms, which meets the real-time demand of autonomous vehicles; (3) The average identification accuracies of our method for car and pedestrian on the moderate level are 89.04% and 78.18% respectively, which outperform most previous methods.
This paper formulates the active suspension control problem as disturbance attenuation problem with output and control constraints. The H∞ performance is used to measure ride comfort such that more general road disturbances can be considered, while time-domain hard constraints are captured using the concept of reachable sets and state-space ellipsoids. Hence, conflicting requirements are specified separately and handled in a nature way. In the framework of Linear Matrix Inequality (LMI) optimization, constrained H∞ active suspensions are designed on half-car models with and without considering actuator dynamics. Analysis and simulation results show a promising improvement on ride comfort, while keeping suspension strokes and control inputs within bounds and ensuring a firm contact of wheels to road.
Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning, and environmental understanding. To precisely extract the irregular road boundaries or those blocked by obstructions on the road from the 3D LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows. First, the 3D LiDAR data is pre-processed, employing the vehicle position and attitude information, and many noise points are deleted. Second, the ground points are quickly separated from the pre-processed point cloud data to reduce the disturbance from the obstacles on the road; this greatly decreases the size of the points cloud to be processed. Third, the candidate points of the road boundaries are searched along the predicted trajectory of the autonomous vehicle and filtered using the unique features of the boundary points. Last, a spline fit model is applied to smoothen the road boundaries. An experiment to test the performance of the proposed algorithm was conducted on the ''Xinda'' autonomous vehicle under various road scenarios. The experimental results show that the average accuracy of the proposed algorithm exceeds 93%, and its average processing time is approximately 36.5 ms/frame, which outperforms most of the state-of-the-art methods. This indicates that the proposed algorithm can robustly extract the road boundary in real time even if there are many obstacles on the road. This algorithm has been tested on ''Xinda'' autonomous vehicle for over 1000 kilometers, and its performance was always stable. INDEX TERMS Autonomous vehicle, 3D LiDAR point cloud, road boundary detection, search boundary point, spline model, obstacle occlusion.
Vehicle platooning has been a major research topic in recent years because of its ability to reduce fuel consumption, enhance road traffic safety and utilize the road more efficiently. A practical and applicable platoon merging maneuver is the key to forming new platoons while ensuring safety and economy. This study proposes merging strategies that consider both safe space and acceleration limitations for two adjacent platoons comprising connected autonomous vehicles (CAVs). The distributed model predictive control (DMPC) algorithm is adopted to design a DMPC 2 controller, which includes 1) a space-making DMPC controller that controls the vehicles in one platoon, i.e. the target platoon, to make space for the vehicles in a second platoon, i.e. the merge platoon, and 2) a DMPC platoon controller that controls the merging vehicles to fill in the space in the target platoon. The former considers the explicit acceleration constraint of the vehicle, making the generated trajectory more feasible, and the latter controls the merge platoon to perform an overall mergence, which reduces the complexity of the merge problem. The low computation load of DMPC makes online computing and real-time control possible in practical scenarios. A simulation study is conducted with different scenarios and parameters, and the results demonstrate that the proposed strategy is more feasible and efficient, and less time-consuming than the existing state-of-the-art methods and have the advantages of taking safety distance and control input constraints into account. INDEX TERMS Platoons merging, space making, distributed model predictive control, connected and autonomous vehicles. I. INTRODUCTION Research on the platooning of connected autonomous vehicles (CAVs) is of great significance in the field of intelligent transportation systems since it has the potential to enhance road safety, improve traffic efficiency, and reduce fuel consumption [1]-[4]. PATH has a long-term commitment to platoon control research, in which many topics are discussed, such as control architecture, control methods, and string stability [5]. Many other related issues The associate editor coordinating the review of this manuscript and approving it for publication was Junhui Zhao.
This paper exploits the multi-objective control technique to design an output-feedback active suspension. The ride performance is characterized by the 'HZ norm, while requirements for having good handling and respecting structural constraint and actuator saturation are specified by the generalized 'HZ norm. The active suspension problem is then formulated in a mixed 'HH~ / generalized 'H2 control problem, and an outputfeedback solution is derived using LMI optimization. Simulation results for a half-car model are discussed.
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