It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.

In this paper, a hybrid fault-tolerant control strategy is putted forward to improve the stability of the four-wheel independent drive (4WID) electric vehicle with motor failures. To improve the handling performance of the vehicle with in-wheel motor failures, the faults of in-wheel motors are analyzed and modeled. Then, a model reference adaptive fault observer was designed to observe the faults in real-time. Based on the observation results, there are designed a model predictive control (MPC) based high-performance active fault-tolerant control (AFTC) strategy and a sliding mode control based high-robust passive fault-tolerant control (PFTC) strategy. However, the fault observation results may not always be accurately. For this circumstance, a hybrid fault-tolerant control strategy was designed to make the control method find a balance between optimality and robustness. Finally, a series of simulations are conducted on a hardware-in-loop (HIL) real-time simulator, the simulation results show that the control strategy designed in this paper is effectiveness.

Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

Current single-stage 3D object detectors often use predefined single points of feature maps to generate confidence scores. However, the point feature not only lacks the boundaries and inner features but also does not establish an explicit association between regression box and confidence scores. In this paper, we present a novel single-stage object detector called keypoint-aware single-stage 3D object detector (KASSD). First, we design a lightweight location attention module (LLM), including feature reuse strategy (FRS) and location attention module (LAM). The FRS can facilitate the flow of spatial information. By considering the location, the LAM adopts weighted feature fusion to obtain efficient multi-level feature representation. To alleviate the inconsistencies mentioned above, we introduce a keypoint-aware module (KAM). The KAM can model spatial relationships and learn rich semantic information by representing the predicted object as a set of keypoints. We conduct experiments on the KITTI dataset. The experimental results show that our method has a competitive performance with 79.74% AP on a moderate difficulty level while maintaining 21.8 FPS inference speed.

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.

Existing environment modeling approaches and trajectory planning approaches for intelligent vehicles are difficult to adapt to multiple scenarios, as scenarios are diverse and changeable, which may lead to potential risks. This work proposes a cognitive spatial–time environment modeling approach for autonomous vehicles, which models a multi-scenario-adapted spatial–time environment model from a cognitive perspective and transforms the scenario-based trajectory planning problem into a unified spatial–time planning problem. The commonality of multiple typical Chinese road scenarios is analyzed, and a unified spatial–time environment model for multi-scenario adaptation is defined and established. The adaptability and trajectory planning potential of the spatial–time environment model are analyzed, and the planning results are obtained through a hybrid A* algorithm. The simulation results show that the proposal is effective in blurring the boundary between scenarios, allowing a single planning approach to adapt to multiple scenarios and plan optimal trajectories (optimal in both path and speed domains) and introducing more flexibility to the planning.