Traditional road testing of autonomous vehicles faces significant limitations, including long
testing cycles, high costs, and substantial risks. Consequently, autonomous driving …

The development of autonomous driving models through reinforcement learning has gained
significant traction. However, develo** obstacle avoidance systems remains a challenge …

Uncertainty has long been a critical area of study in robotics, particularly when robots are
equipped with analytical models. As we move towards the widespread use of deep neural …

Scenario simulation plays an integral role in the development, application, and management
of intelligent vehicles. However, planning agents and customizing scenarios for complex …

The planning and control subsystem is essential to the autonomous driving system. The
prevailing algorithmic architecture follows a hierarchical design: the planning subsystem …

Recent advancements in vehicle technology have stimulated innovation across the
automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving …

Thoroughly testing autonomy systems is crucial in the pursuit of safe autonomous driving
vehicles. It necessitates creating safety-critical scenarios that go beyond what can be safely …

Advanced Driver Assistance Systems (ADAS) and autonomous driving require high
positioning performance (high accuracy, reliability, and availability). These requirements are …

Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are
widely used in autonomous vehicles for critical perception tasks such as object detection …

Closed-loop simulation environments play a crucial role in the validation and enhancement
of autonomous driving systems (ADS). However, certain challenges warrant significant …