Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surrounding the target vehicle, and then the row and the column that will be occupied by the target vehicle at future time steps are determined using the RF algorithm and the LSTM encoder-decoder architecture, respectively. For the collection of training data, the test vehicle was equipped with a camera and LIDAR sensors along with vehicular wireless communication devices, and the experiments were conducted under various driving scenarios. The vehicle test results demonstrate that the proposed method provides more robust trajectory prediction compared with existing trajectory prediction methods.
Driving environment perception for automated vehicles is typically achieved by the use of automotive remote sensors such as radars and cameras. A vehicular wireless communication system can be viewed as a new type of remote sensor that plays a central role in connected and automated vehicles (CAVs), which are capable of sharing information with each other and also with the surrounding infrastructure. In this paper, we present the design and implementation of driving environment perception based on the fusion of vehicular wireless communications and automotive remote sensors. A track-to-track fusion of high-level sensor data and vehicular wireless communication data was performed to accurately and reliably locate the remote target in the vehicle surroundings and predict the future trajectory. The proposed approach was implemented and evaluated in vehicle tests conducted at a proving ground. The experimental results demonstrate that using vehicular wireless communications in conjunction with the on-board sensors enables improved perception of the surrounding vehicle located at varying longitudinal and lateral distances. The results also indicate that vehicle future trajectory and potential crash involvement can be reliably predicted with the proposed system in different cut-in driving scenarios.
Recently, the interest regarding Drive-thru Internet systems has been rapidly arising in industrial and academic fields in view of the widespread adoption of IEEE 802.11 networks and its great potential to provide cost-effective Internet access. Drive-thru Internet systems are multiple-access wireless networks in which users in moving vehicles request/receive services such as digital map update and MP3 download to/from a Road Side Unit (RSU) as the vehicles pass through the coverage range of the RSU. For the purpose of efficiently supporting various services, Wireless Access in Vehicular Environment (WAVE), which is the standard for VANETs communications, specifies multichannel utilization, where the overall bandwidth is subdivided into seven channels, namely, one Control Channel (CCH) and six Service Channels (SCHs). However, originally designed for quasi-static single-channel-based small-scale indoor applications, the performance of IEEE 802.11 in the outdoor vehicular environment, where a large number of fast-moving vehicles simultaneously contend for channel access in the multichannel environment, is still unclear. In this paper, a unified analytical framework is established to study the performance of multichannel Drive-thru Internet systems. Specifically, taking account of channel access contention of vehicles and power reception probability of an RSU, the message arrival rate at the RSU on the uplink channel (i.e., CCH) is derived. Then, a multiserver queueing model, which plays the role of a bridge connecting the uplink and downlink (i.e., SCHs) communications, is developed for the purpose of accurately capturing the dynamics of the multichannel environment. Based on the developed framework, it can be noticed that as the intensity of channel contention increases, the saturated throughput of SCHs decreases rapidly, and the system becomes unstable due to the reason that vehicles have to wait for a very large amount of time to receive the requested service messages, or even worse, cannot receive the messages before leaving the coverage of RSU. In order to keep the throughput at the maximum level regardless of the channel contention intensity while maintaining the system stability, we propose a centralized coordination mechanism. Simulation experiments are carried out to validate the accuracy of the developed analytical framework and the effectiveness of the proposed centralized coordination mechanism. INDEX TERMS performance modeling, performance analysis, Drive-thru network, IEEE 802.11 DCF, vehicular ad-hoc networks (VANETs).
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