Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

A closed-loop parallel testing system implements more challenging tests to accelerate evaluation and development of autonomous vehicles.

End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.

With the rapid development of autonomous driving technologies, a vast array of autonomous driving algorithms and platforms have emerged. These algorithms and platforms are usually designed for different tasks in specific scenarios, which makes them incompatible with each other. To solve the problem, we propose a ubiquitous operating system named Parallel Driving Operating System (PDOS) based on the parallel driving theory. Specifically, the PDOS architecture includes the hardware layer, the kernel layer, the functional layer, and the application layer, which can realize heterogeneous hardware support, ubiquitous resource management, algorithm optimization, and ubiquitous application development, respectively. Moreover, we derive the PDOS into specific operating systems in four application scenarios, i.e., intelligent mining, intelligent warehousing, intelligent logistics, and intelligent port. To validate the PDOS, we test its usability, reliability, and efficiency in these scenarios. Evaluation results demonstrate the high performance of PDOS in multiple autonomous driving, and indicate that PDOS is an efficient operating system for parallel driving.

In face of the safety risk and ageing workforce in traditional mining industry, smart mining is becoming the new trend, especially the autonomous mining, which could completely address the safety issue while boosting efficiency. However, the lack of specific standards for the smart mining has led to the various types of equipment and high development costs. Therefore, we present the effort by Chinese mining research organizations and industrial enterprises in creating standards for automation and autonomy. Besides, we introduce a few key intelligent technologies for the autonomous mining transportation, and several current applications of autonomous trucks in mines around the world. These key intelligent technologies are likely to be further improved with following standards and become more affordable for mines.