3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to track the irregular motion of objects for LiDAR-based methods accurately. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection. For occlusion problems, we are the first to propose an occlusion head to select the best object appearance features multiple times effectively, reducing the influence of occlusions. To decrease the impact of false detection in tracking, we design a motion cost matrix based on confidence scores which improve the positioning and object prediction accuracy in 3D space. As existing multi-object tracking methods always evaluate each category separately and do not consider the mismatch between objects of different categories, we also propose to build a multi-category cost to implement multi-object tracking in multi-category scenes. A series of validation experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our proposed method achieves state-of-the-art performance with 79.99% HOTA and the lowest identity switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal MOT methods on the KITTI test dataset. And our method achieves state-of-the-art performance among all algorithms on the nuScenes test dataset with 75.3% AMOTA.

Multi-modal fusion plays a critical role in 3D object detection, overcoming the inherent limitations of single-sensor perception in autonomous driving. Most fusion methods require data from high-resolution cameras and LiDAR sensors, which are less robust and the detection accuracy drops drastically with the increase of range as the point cloud density decreases. Alternatively, the fusion of Radar and LiDAR alleviates these issues but is still a developing field, especially for 4D Radar with a more robust and broader detection range. Nevertheless, different data characteristics and noise distributions between two sensors hinder performance improvement when directly integrating them. Therefore, we are the first to propose a novel fusion method termed $M^{2}$ -Fusion for 4D Radar and LiDAR, based on Multi-modal and Multi-scale fusion. To better integrate two sensors, we propose an Interaction-based Multi-Modal Fusion (IMMF) method utilizing a self-attention mechanism to learn features from each modality and exchange intermediate layer information. Specific to the current single-resolution voxel division's precision and efficiency balance problem, we also put forward a Center-based Multi-Scale Fusion (CMSF) method to first regress the center points of objects and then extract features in multiple resolutions. Furthermore, we present a data preprocessing method based on Gaussian distribution that effectively decreases data noise to reduce errors caused by point cloud divergence of 4D Radar data in the $x$ - $z$ plane. To evaluate the proposed fusion method, a series of experiments were conducted using the Astyx HiRes 2019 dataset, including the calibrated 4D Radar and 16-line LiDAR data. The results demonstrated that our fusion method compared favorably with state-of-the-art algorithms. When compared to PointPillars, our method achieves mAP (mean average precision) increases of 5.64 $\%$ and 13.57 $\%$ for 3D and BEV (bird's eye view) detection of the car class at a moderate level, respectively.

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely generative pre-trained transformer 3 (GPT-3), is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.

Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' attention in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this paper, we introduce the existing application fields of vehicle information and the research progress of related methods, as well as the multi-modal fusion methods based on information. We also introduced the relevant information of the vehicle information dataset in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle information.