Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this paper proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception.

Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks? Existing methods ignore two important facts: 1) different models have heterogeneous demands on training data; 2) there is a mismatch between the simulated environment and the realworld environment. As a result, they could lead to low learning performance in practice. This paper proposes the learning centric wireless resource allocation (LCWRA) scheme that maximizes the worst learning performance of multiple classification tasks. Analysis shows that the optimal transmission time has an inverse power relationship with respect to the classification error. Finally, both simulation and experimental results are provided to verify the performance of the proposed LCWRA scheme and its robustness in real implementation.

Environment perception is one of the major issues in autonomous driving systems. In particular, effective and robust drivable road region detection still remains a challenge to be addressed for autonomous vehicles in multi-lane roads, intersections and unstructured road environments. In this paper, a computer vision and neural networks-based drivable road region detection approach is proposed for fixed-route autonomous vehicles (e.g., shuttles, buses and other vehicles operating on fixed routes), using a vehicle-mounted camera, route map and real-time vehicle location. The key idea of the proposed approach is to fuse an image with its corresponding local route map to obtain the map-fusion image (MFI) where the information of the image and route map act as complementary to each other. The information of the image can be utilized in road regions with rich features, while local route map acts as critical heuristics that enable robust drivable road region detection in areas without clear lane marking or borders. A neural network model constructed upon the Convolutional Neural Networks (CNNs), namely FCN-VGG16, is utilized to extract the drivable road region from the fused MFI. The proposed approach is validated using real-world driving scenario videos captured by an industrial camera mounted on a testing vehicle. Experiments demonstrate that the proposed approach outperforms the conventional approach which uses non-fused images in terms of detection accuracy and robustness, and it achieves desirable robustness against undesirable illumination conditions and pavement appearance, as well as projection and map-fusion errors.

Reinforcement learning (RL) offers a powerful paradigm for adaptive motion planning in complex driving environments. However, applying RL to autonomous driving remains challenging due to uncertainty from partial observability and the stochastic, multimodal behaviors of traffic participants. This paper presents a novel risk-aware RL framework for non-conservative motion planning under uncertainty. By integrating Partially Observable Markov Decision Processes (POMDP) with a deep RL-based policy optimization scheme, the proposed approach explicitly models aleatoric uncertainty via a Gaussian Mixture Bayesian Belief Updater and a time-varying risk field. Additionally, an Adaptive Context-aware Attention (ACA) module is employed to prioritize critical targets for enhanced interaction modeling dynamically. Extensive experiments on the CARLA simulator show that the framework generalizes well across diverse traffic conditions, improving average reward by 65.74% and 64.02% in low-speed dense and high-speed sparse scenarios. It remains robust in challenging situations such as overtaking and sudden lane changes in the PeMS dataset. Furthermore, distributed deployment tests confirm a real-time performance of 10 Hz on a hardware-in-the-loop platform, demonstrating the feasibility of practical deployment.

Sampling-based methods such as Rapidly Exploring Random Trees (RRT) and Probabilistic Road Maps (PRM) have been recognized as effective tools to solve the path planning problem for both ground mobile robots and flying robots in high-dimensional configuration space. However, the efficiency of the RRT planner will be decreased in complex environments with narrow passages. This paper presents a multiple RRTs-based path planning framework to improve the above mentioned problem. The key ingredient of the framework is a hybrid sampling strategy which takes advantage of the Randomized Star Builder (RSB) and uniform sampling. The RSB method can efficiently recognize narrow passage regions while avoiding unnecessary samples in the corners and dead ends, and generates milestones for growing multiple local trees from narrow passages. Moreover, uniform sampling is used to generate global RRT trees in order to capture global connectivity. Simulation results of 3D flying robots demonstrate the effectiveness of the proposed method. Comparisons between the proposed method and other RRT-based planners are also presented.