Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained
increasing interest from both researchers and companies. This has caused a rapid …

Birds-Eye-View (BEV) perception can naturally represent natural scenes, which is conducive
to multimodal data processing and fusion. BEV data contain rich semantics and integrate the …

Autonomous driving related researches require the analysis and usage of massive amounts
of driving scenario data. Compared to raw data collected by sensors, scenario data provide …

The potential of the Internet of Vehicles (IoV) to reduce on-board system costs in
autonomous vehicles through shared intelligence is considerable. However, it still faces …

Multitask learning has led to great success in many deep learning applications during the
last decade. However, recent experiments have demonstrated that the performance of …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Monocular depth estimation has attracted extensive attention and made great progress in
recent years. However, the performance still lags far behind LiDAR-based depth completion …

Different from a unimodal model whose input is from a single modality the input (called multi-
modal input) of a multi-modal model is from multiple modalities such as image 3D points …

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques for
imitation learning proves inadequate in challenging situations that involve numerous …