Zobrazeno 1 - 10
of 643
pro vyhledávání: '"Van Arem, Bart"'
The integration of autonomous vehicles (AVs) into transportation systems presents an unprecedented opportunity to enhance road safety and efficiency. However, understanding the interactions between AVs and human-driven vehicles (HVs) at intersections
Externí odkaz:
http://arxiv.org/abs/2410.12538
The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to
Externí odkaz:
http://arxiv.org/abs/2402.08080
Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adop
Externí odkaz:
http://arxiv.org/abs/2312.04610
The burgeoning navigation services using digital maps provide great convenience to drivers. Nevertheless, the presence of anomalies in lane rendering map images occasionally introduces potential hazards, as such anomalies can be misleading to human d
Externí odkaz:
http://arxiv.org/abs/2312.04398
Autor:
Liao, Fanchao, Vleugel, Jaap, Bösehans, Gustav, Dissanayake, Dilum, Thorpe, Neil, Bell, Margaret, van Arem, Bart, Correia, Gonçalo Homem de Almeida
Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switc
Externí odkaz:
http://arxiv.org/abs/2310.19036
A shared space area is a low-speed urban area in which pedestrians, cyclists, and vehicles share the road, often relying on informal interaction rules and greatly expanding freedom of movement for pedestrians and cyclists. While shared space has the
Externí odkaz:
http://arxiv.org/abs/2308.05654
Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from intera
Externí odkaz:
http://arxiv.org/abs/2306.11465
Ride-sourcing drivers as individual service suppliers can freely adopt their own relocation strategies including waiting, cruising freely, or following the platform recommendations. These decisions substantially impact the balance between supply and
Externí odkaz:
http://arxiv.org/abs/2208.02323
The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for saf
Externí odkaz:
http://arxiv.org/abs/2203.01003
Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately dete
Externí odkaz:
http://arxiv.org/abs/2110.04079