|
|
|
|
|
Jizhao Wang, Yunyi Liang, Jinjun Tang and Zhizhou Wu
This research contributes to the development of a technological method to obtain highly accurate vehicle trajectory data. The reconstructed trajectory data play a key role in traffic state prediction, traffic management and the decision making of autonom...
ver más
|
|
|
|
|
|
|
|
|
Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is ...
ver más
|
|
|
|
|
|
|
|
|
Shichen Fu, Zhenhua Yang, Yuan Ma, Zhenfeng Li, Le Xu and Huixing Zhou
Detecting the factors affecting drivers? safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distractio...
ver más
|
|
|
|
|
|
|
|
|
Eunkyu Lee, Junaid Khan, Umar Zaman, Jaebin Ku, Sanha Kim and Kyungsup Kim
With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation syste...
ver más
|
|
|
|
|
|
|
|
|
Abderrazzaq Kharroubi, Zouhair Ballouch, Rafika Hajji, Anass Yarroudh and Roland Billen
Railway scene understanding is crucial for various applications, including autonomous trains, digital twining, and infrastructure change monitoring. However, the development of the latter is constrained by the lack of annotated datasets and limitations o...
ver más
|
|
|
|