On the importance of real data for microscopic urban vehicular mobility trace

Lèbre, Marie-Ange; Mouël, Frédéric Le; Ménard, Éric

doi:10.1109/itst.2015.7377394

Cited by 26 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The moving speeds of ground targets are generally much lower than those of UAVs, and they often make stop-go-turn maneuvers with a much smaller turn radius. This section describes a numerical simulation that was performed using realistic ground vehicle data that were acquired at 1 Hz in the town of Creteil, France (Lebre et al, 2015). The trajectory and speed of the ground vehicle are shown in Figure 12.…”

Section: Simulation Resultsmentioning

confidence: 99%

Over-flight and standoff tracking of a ground target with a fixed-wing unmanned aerial vehicle based on a unified sliding mode guidance law

Lin

Shi

Lyu

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Target tracking of ground targets is a significant application of unmanned aerial vehicles (UAVs) in civil and military fields. There are two common modes for target tracking: over-flight tracking and standoff tracking. Each tracking method has a wide application prospect. However, many researchers have studied these two tracking methods separately and designed different guidance laws, which is not conducive to practical application. In this paper, a new guidance law based on sliding mode guidance (SMG) is proposed for tracking a stationary target, which is compatible with the two tracking modes. The stability and finite-time convergence of the guidance law are proved. Then, the guidance is extended to tracking a moving target. The numerical simulations are carried out for the tracking problems of ground targets, and the results verify the effectiveness of the proposed guidance law.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Over-flight and standoff tracking of a ground target with a fixed-wing unmanned aerial vehicle based on a unified sliding mode guidance law

Lin

Shi

Lyu

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…We use TensorFlow 1.15.0 with Python 3.6 to implement SA-DQN algorithm. In the experiment, we consider the real vehicle trajectory data set of two hours in the Random sampling minibatch of experience morning (7.00-9.00) on a circular road in cretey, France [38]. The communication radius of the vehicle is 130 meters.…”

Section: Parameter Settingsmentioning

confidence: 99%

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Lin

et al. 2021

J Cloud Comp

View full text Add to dashboard Cite

Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

show abstract

“…Our method analyzes different causes identified in the video recordings, other parameters such as the weather, building architectures, disabled people identification, vehicle conditions could be integrated and extend the method to global system dynamics. This pedestrian behaviour analysis can also provide urban services such as traffic optimization [16,15], smart parking [17], taxi recommendation [23], crisis management [14].…”

Section: Future Workmentioning

confidence: 99%

Datacentric analysis to reduce pedestrians accidents: A case study in Colombia

Puentes,

Novoa,

Nivia

et al. 2021

Preprint

View full text Add to dashboard Cite

Since 2012, in a case-study in Bucaramanga-Colombia, 179 pedestrians died in car accidents, and another 2873 pedestrians were injured. Each day, at least one passerby is involved in a tragedy. Knowing the causes to decrease accidents is crucial, and using system-dynamics to reproduce the collisions' events is critical to prevent further accidents. This work implements simulations to save lives by reducing the city's accidental rate and suggesting new safety policies to implement. Simulation's inputs are video recordings in some areas of the city. Deep Learning analysis of the images results in the segmentation of the different objects in the scene, and an interaction model identifies the primary reasons which prevail in the pedestrians or vehicles' behaviours. The first and most efficient safety policy to implement -validated by our simulations -would be to build speed bumps in specific places before the crossings reducing the accident rate by 80%.

show abstract

On the importance of real data for microscopic urban vehicular mobility trace

Cited by 26 publications

References 8 publications

Over-flight and standoff tracking of a ground target with a fixed-wing unmanned aerial vehicle based on a unified sliding mode guidance law

Over-flight and standoff tracking of a ground target with a fixed-wing unmanned aerial vehicle based on a unified sliding mode guidance law

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Datacentric analysis to reduce pedestrians accidents: A case study in Colombia

Contact Info

Product

Resources

About