相关论文: CMTS: Conditional Multiple Trajectory Synthesizer …
Strategies that include the generation of synthetic data are beginning to be viable as obtaining real data can be logistically complicated, very expensive or slow. Not only the capture of the data can lead to complications, but also its…
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the…
A significant challenge in autonomous racing is to generate overtaking maneuvers. Racing agents must execute these maneuvers on complex racetracks with little room for error. Optimization techniques and graph-based methods have been…
Synthesis of diverse driving scenes serves as a crucial data augmentation technique for validating the robustness and generalizability of autonomous driving systems. Current methods aggregate high-definition (HD) maps and 3D bounding boxes…
Advancements in Autonomous Driving Systems (ADS) have brought significant benefits, but also raised concerns regarding their safety. Virtual tests are common practices to ensure the safety of ADS because they are more efficient and safer…
Datasets are essential to train and evaluate computer vision models used for traffic analysis and to enhance road safety. Existing real datasets fit real-world scenarios, capturing authentic road object behaviors, however, they typically…
Path planning for autonomous vehicles in arbitrary environments requires a guarantee of safety, but this can be impractical to ensure in real-time when the vehicle is described with a high-fidelity model. To address this problem, this paper…
Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these…
Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions between road users and the limited availability of diverse, large-scale datasets. To…
Traffic flow simulation within the domain of intelligent transportation systems is garnering significant attention, and generating realistic, diverse, and human-like traffic patterns presents critical challenges that must be addressed.…
As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Motion, provide realistic recorded scenarios for model…
Autonomous driving systems (ADS) require extensive testing and validation before deployment. However, it is tedious and time-consuming to construct traffic scenarios for ADS testing. In this paper, we propose TrafficComposer, a multi-modal…
Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the…
The validation of autonomous driving systems benefits greatly from the ability to generate scenarios that are both realistic and precisely controllable. Conventional approaches, such as real-world test drives, are not only expensive but…
Generating representative rear-end crash scenarios is crucial for safety assessments of Advanced Driver Assistance Systems (ADAS) and Automated Driving systems (ADS). However, existing methods for scenario generation face challenges such as…
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle…
Generating multi-vehicle interaction scenarios can benefit motion planning and decision making of autonomous vehicles when on-road data is insufficient. This paper presents an efficient approach to generate varied multi-vehicle interaction…
We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then, we develop a mixed-integer conic approximation to the…
Trajectory prediction is an important task, especially in autonomous driving. The ability to forecast the position of other moving agents can yield to an effective planning, ensuring safety for the autonomous vehicle as well for the…
Driving-related safety-critical events (SCEs), including crashes and near-crashes, provide essential insights for the development and safety evaluation of automated driving systems. However, two major challenges limit their accessibility:…