Automatic Valet Parking (AVP) has garnered significant attention from industry and academia due to its potential to enhance traffic efficiency, parking safety, and user experience. While AVP technologies have been successfully applied in standard parking scenarios with clear markings, real-world parking environments are far more diverse and complex, posing challenges for current systems. To address these limitations, we present Parking-SG, an open-vocabulary hierarchical 3D scene graph representation, facilitating the application of AVP in open and complex environments. Our approach builds an object-based, open-vocabulary map that integrates both ground-level and ground-above objects for comprehensive environmental understanding. Leveraging common sense reasoning and object behavior relationships, various standard or non-standard parking spaces are inferred in open environments. Additionally, we extract and analyze path topology to construct a hierarchical map representation, supporting complex AVP tasks. Parking-SG is validated in both simulated and real-world environments, demonstrating its ability to generate rich environmental representations, accurately and flexibly infer parking spaces, and effectively perform complex AVP tasks.

Rapid acquisition of environmental parameters of emergency sites is crucial for emergency response. However, in areas where public ground networks do not provide sufficient coverage or are destroyed by disasters, the rapid backhaul of monitoring data from emergency sites is difficult. Due to the urgent demand for data collection in such areas, we propose an unmanned aerial vehicle (UAV)-aided emergency environmental monitoring system using a LoRa mesh networking approach, which can quickly build a large range of wireless communication networks suitable for small data transmission. First, we designed a LoRa-based mesh network protocol by using a custom slotted ALOHA medium access mechanism. Second, we designed and implemented a custom software and hardware platform of the monitoring system. The system adopts a communication mode, including multiple sensor nodes (SNs), multiple relay nodes (RNs), a gateway (GW), and a network server. Finally, we demonstrated the performance of the protocol and the system by in-lab and outdoor tests. The results show that when data rate is 0.293 kb/s, the line-of-sight 1-hop transmission distance is more than 10 km, and the 1-hop transmission delay is less than 6 s; the end-to-end delay has an upper bound, which can meet the real-time requirement of emergency response; the least upper bound of throughput per slot of our mesh protocol is much higher than that of LoRaWAN. Evaluation results also demonstrate the network robustness to environmental changes.

Existing autonomous parking solutions usually require special signs, pre-built maps or accurate ranging sensors to achieve reliable perception of the parking environment, but these methods are difficult to popularize because they either require preconditions or are expensive for production cars. In this paper, we propose a vision-only autonomous parking solution based on only six cameras. Through the appropriate depth estimation algorithms, our method obtains the pixel level depth of the image, and constructs a dense point cloud, so as to realize the fine perception of the parking environment. An improved Radon transform based parking space detection method are applied for better parking space detection method. Our proposed method achieves processing speed of above 5 Hz on a intermediate level computing platform. Furthermore, we demonstrate the practicability of the proposed system in real-world parking lots.

Considering the problem of rapid collection and reliable transmission of emergency information, as well as the emergency response missions and natural environment characteristics of emergency sites, this article proposes a novel unmanned aerial vehicle (UAV) path planning framework that considers waypoint and motion. First, the waypoint planning improves the reliability of the uplink and the downlink data transmission to the greatest extent by jointly optimizing UAVs 3-D position and transmit power of UAV and users' equipment (UE). Second, the motion planning allows the UAVs to traverse all waypoints in the shortest amount of time, which allows more time for data collection by maximizing the acceleration of each UAV. Our results show that the proposed path planning algorithm can significantly improve the normalized Quality-of-Experience (QoE) index and effectively shorten the flight time of each UAV for different assignments. Therefore, the method proposed in this article can effectively solve the key problems in the technical system of emergency response and provide a scientific decision-making basis for the post-disaster emergency response.