SIND: A Drone Dataset at Signalized Intersection in China

Xu, Ya‐Qiong; Shao, Wenbo; Li, Jun; Yang, Kai; Wang, Weida; Huang, Hua; Lv, Chen; Wang, Hong

doi:10.1109/itsc55140.2022.9921959

Cited by 29 publications

(5 citation statements)

References 37 publications

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“…The speed of the ego vehicle entering the intersection is set to 20 km/h. The width of the lanes is 3.5 m. The traffic light signal stages of green, red and yellow light in the simulation are set to 26 s, 31 s, and 3 s, according to an actual recorded two-lane intersection in the SinD dataset [40]. Software-in-the-loop (SIL) testing is conducted to verify if the C++ code corresponding to the controller model accurately executes identical control actions when subjected to equivalent stimuli.…”

Section: Case Studymentioning

confidence: 99%

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Mu,

Yu,

et al. 2024

Applied Sciences

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In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.

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Section: Case Studymentioning

confidence: 99%

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Mu,

Yu,

et al. 2024

Applied Sciences

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“…However, gathering these real datasets requires significant effort for many practical reasons, including privacy, obtaining permission, and severe weather conditions. Currently, several publicly available datasets exist for VANET scenarios [10][11][12][13][14][15][16] and Table 1 summarises these available datasets. Most available datasets focused on intersections, as shown in Table 1 because simulating intersection scenarios is crucial since most road accidents happen near intersections due to complex and challenging driving conditions.…”

Section: Background and Summarymentioning

confidence: 99%

CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany

Karunathilake,

Zongo,

Amarawardana

et al. 2024

Sci Data

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Vehicular Ad-Hoc Networks (VANETs) were introduced to avoid vehicular-related accidents and to improve the safety of both vehicular passengers and other road users. In VANETs, the vehicles are expected to communicate with neighbouring vehicles to increase awareness about the surrounding by using V2V (vehicle-to-vehicle) communication links. Since the introduction of VANETs, much research has focused on developing state-of-the-art algorithms to increase safety. However, real-world testing of these developed algorithms has become challenging due to the required high cost and multiple practical reasons. Therefore, simulation-based testing is commonly used for VANETs related applications. Using real datasets inside a simulation can significantly increase the results’ accuracy and help to achieve realistic results. In this study, we present a dataset called ’CN+’, which consists of more than 25,000 vehicles collected over 32 hours at a signalized intersection in Bremen, Germany. paper

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“…The openDD dataset [13] is collected in Germany in 2020. openDD contains 84,774 trajectories in 62 hours and HD map data of seven different unsignalized roundabouts. At the signalized intersection in China, a drone dataset SIND [14] was collected and published in 2022. SIND includes traffic light states and HD maps, which contain 7 hours of recording including 13,248 trajectories and include 7 road user types: cars, trucks, buses, tricycles, bikes, motorcycles, and pedestrians.…”

Section: Previous Workmentioning

confidence: 99%

Evaluating Data Sources and Datasets in Intelligent Transport Systems Through a Weighted Scoring Model

Bouhsissin,

Sael,

Benabbou

2023

IJTDI

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Global road transport safety concerns are escalating, evidenced by an annual increase in traffic-related accidents, fatalities, and injuries. In response, numerous governmental road safety initiatives aim to mitigate crash incidences and consequent harm. Extant literature documents myriad datasets collated to address road safety challenges and bolster intelligent transport systems (ITS). These datasets are amassed via diverse measurement modalities, including cameras, radar sensors, and unmanned aerial vehicles (UAVs), commonly known as drones. This study delineates ITS datasets pertinent to transport issue resolution and elucidates the measurement methodologies employed in dataset accrual for ITS. A dual comparative analysis forms the core of this research: the first examination juxtaposes data source methodologies for dataset collection, while the second compares disparate datasets. Both examinations are conducted using the Weighted Scoring Model (WSM). Criteria germane to the comparison are meticulously defined, and respective weights are assigned, mirroring their significance. Findings reveal the UAV-based method as superior in amassing datasets pertinent to drivers and vehicles. Among the datasets evaluated, the SinD dataset secures the preeminent position. This methodical approach facilitates astute decisions regarding data source and dataset selection, augmenting the comprehension of their efficacy and relevance within the ITS domain.

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SIND: A Drone Dataset at Signalized Intersection in China

Cited by 29 publications

References 37 publications

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany

Evaluating Data Sources and Datasets in Intelligent Transport Systems Through a Weighted Scoring Model

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