Multi-Agent Path Finding (MAPF) is an important issue in multi-agent cooperation. Many studies apply MultiAgent Reinforcement Learning (MARL) to solve MAPF in partially observable settings. The objective of cooperative MARL is to maximize the cumulative team reward. Nevertheless, in partially observable settings, the team reward is misleading due to unpredictable factors from the behavior and state of unobserved agents. To address this issue, we propose a Partially Observable Altruistic Q-learning (POAQL) method. POAQL considers the cumulative reward of the observed subteam instead of the whole team, where Altruistic Q-learning plays an important role in learning the subteam action value. In addition, we design a new conflict resolution without additional guidance to emphasize the cooperative nature of MARL frameworks. Experimental results show that POAQL outperforms existing reinforcement learning methods in terms of efficiency and performance.

High-definition (HD) map is one of the key assets for autonomous driving, which supports various modules such as behavior prediction and motion planning of autonomous vehicles by providing accurate and rich geometric and semantic information. However, at present, the scalability and computational efficiency of HD map generation cannot meet the needs of highly automated driving. Specifically, efficiently obtaining the optimal parameters of the road’s reference path is still an open problem. In this paper, we propose a fast and robust path reconstruction method, which compresses the dense points of a reference line into sparse parameters with minimal loss of information. The reconstructed path consists of segmented linear curvature contours, which are straight lines, circular arcs, and clothoids. The optimum result is obtained through linear programming for short-path reconstruction, and for the long paths, a fast progressive reconstruction approach is used to find a feasible solution. Experimental results on both randomly generated data and the GPS-collected trajectories show that compared with existing methods, the proposed method can generate more accurate path reconstruction, and the computational time is greatly reduced.

Feature Pyramid Network (FPN) has been widely adopted to exploit multi-scale features for scale variation in object detection. However, intrinsic defects in most of the current methods with FPN make it difficult to adapt to the feature of different geometric objects. To address this issue, we introduce geometric prior into FPN to obtain more discriminative features. In this paper, we propose Geometry-aware Feature Pyramid Network (GaFPN), which mainly consists of the novel Geometry-aware Mapping Module and Geometry-aware Predictor Head.The Geometry-aware Mapping Module is proposed to make full use of all pyramid features to obtain better proposal features by the weight-generation subnetwork. The weights generation subnetwork generates fusion weight for each layer proposal features by using the geometric information of the proposal. The Geometry-aware Predictor Head introduces geometric prior into predictor head by the embedding generation network to strengthen feature representation for classification and regression. Our GaFPN can be easily extended to other two-stage object detectors with feature pyramid and applied to instance segmentation task. The proposed GaFPN significantly improves detection performance compared to baseline detectors with ResNet-50-FPN: +1.9, +2.0, +1.7, +1.3, +0.8 points Average Precision (AP) on Faster-RCNN, Cascade R-CNN, Dynamic R-CNN, SABL, and AugFPN respectively on MS COCO dataset.

Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The reason is all points on the path are considered as decision variables, which leads to the high dimensionality of the defined optimization problem. Therefore, we propose a novel global path optimization method. The method characterizes the path as a parametric curve and then optimizes the curve's parameters with a defined objective function, which successfully reduces the dimension of optimization problem. The proposed method is compared with baseline and state-of-the-art methods. Experimental results show the path optimized by our method is not only optimal in collision risk, but also in efficiency and smoothness. Furthermore, the proposed method is also implemented and tested in both simulation and real robots.

Autonomous driving systems (ADSs) need to be able to respond quickly to changes in the dynamic traffic scenario. However, regardless of the changes occurring in traffic scenes, the current local path planning frameworks of ADSs are based on the fixed frequency re-planning path (i.e., running their planning algorithms repeatedly). This planning method makes it difficult to provide a reasonable traveling path, agility, and comfort for driverless vehicles in changing traffic scenarios. Therefore, this article performs an in-depth analysis of the problems of traditional planning frameworks which use a fixed frequency to replan the path and proposes a novel path planning framework that is universal based on multiple-models. The proposed framework divides the planning process into several layers, each of which has different functions. With this framework, the ADS can adaptively adjust the planning process according to the changes in traffic scenes and then provide different path planning algorithms to ensure its safety and flexibility in the process of driving. Moreover, the problems caused by the traditional planning framework can be solved. This framework has been applied to the autonomous vehicle “Pioneer”, which won first place in the 2019 China Intelligent Vehicle Future Challenge (IVFC). The effectiveness and rationality of the integrated framework of local path planning proposed in this article were verified by a large number of tests in real-world traffic scenarios.

Motion planning algorithms, an essential part of the autonomous driving system, have been extensively studied. However, in large-scale complex scenarios, how to develop an optimal path to comply with the requirements of smoothness and safety remains a vital issue. In this study, a hierarchical search spacial scales-based hybrid A* (termed as HHA*) motion planning method is proposed, capable of efficiently generating smooth and safe paths. The proposed HHA* method covers two stages. First, the search space is divided on a coarse scale to generate local goals. Subsequently, the novel heuristic function and exploration strategies are adopted in the fine-scale search space to generate paths like that with a human driver guided by the local goals. Moreover, with the usage of the clothoid, the smoothness of the generated path is improved to be G2–continuous (i.e., curvature continuous), which fits the vehicle’s kinematic constraints without the need for later smoothing. Numerous experimental results from the simulation and on-road tests indicate that the proposed method can effectively perform motion planning that meets smoothness and safety in large-scale complex scenarios.

The current test of autonomous driving technology requires extensive experimental verification, whether in simulation or on real roads. Netherthless, how to test autonomous vehicles thoroughly in a safe and comprehensive manner remains a major challenge. To achieve safer and more effective autonomous driving testing, this paper proposes a novel mixed test environment-based validation method with vehicle in the loop(ViL) : (1) our method supports more realistic drive safety tests in mixed scenarios which integrate the synthetic and the real-world scenarios. Synthetic scenarios offer complex traffic simulation with diverse road conditions. The real-world scenarios introduce the real autonomous driving vehicle, the real sensor suite as well as the test field to the test loop, having further bridged the gap between Hardware-in-the-Loop(HiL) testing and real road tests than ViL; (2) virtual perceptional results are simulated directly and delivered to the real vehicle in Unified Fusion Data Format(UFDF), without rendering virtual detection data for reduced resource consumption; (3) diverse test scenarios are configurable and reproducible with OSM-based High Definition(HD) map, enabling the simulation to be decoupled from a specific test filed or traffic facilities. A series of experiments on the application of our method have been demonstrated, and our approach is proved to be a promising drive safety testing technique before actual road testing.

Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform `Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the `Pioneer' successfully completed all the races and won the championship without any manual intervention.

In the field of autonomous driving, multi-beam light detection and ranging (3D LIDAR) system and global navigation satellite system/integrated inertial navigation system (GNSS/INS) are widely used in high-definition map construction, localization and obstacle detection. As 3D LIDAR system and INS have their own coordinate systems, the calibration of the two mentioned systems is required. In this paper, a novel algorithm for calibrating the coordinate system of 3D LIDAR and INS is proposed, which consists of three parts. The first procedure is to project two point clouds to the world coordinate system based on the initial transform matrix between 3D LIDAR and INS with the real-time data from INS. Then optimal point-to-point correspondences can be found between two frames of point cloud data through registration method. Finally, the loss function is constructed with the sum of the Euclidean distances of the corresponding points and optimized by using perturbation model of Lie algebras, so as to obtain the optimal transform matrix. With different given initial calibration parameters, test results of both simulation and real experiments validate the proposed algorithm and quantify its accuracy and robustness.

With the development of autonomous driving, offline testing remains an important process allowing low-cost and efficient validation of vehicle performance and vehicle control algorithms in multiple virtual scenarios. This paper aims to propose a novel simulation platform with hardware in the loop (HIL). This platform comprises of four layers: the vehicle simulation layer, the virtual sensors layer, the virtual environment layer and the Electronic Control Unit (ECU) layer for hardware control. Our platform has attained multiple capabilities: (1) it enables the construction and simulation of kinematic car models, various sensors and virtual testing fields; (2) it performs a closed-loop evaluation of scene perception, path planning, decision-making and vehicle control algorithms, whilst also having multi-agent interaction system; (3) it further enables rapid migrations of control and decision-making algorithms from the virtual environment to real self-driving cars. In order to verify the effectiveness of our simulation platform, several experiments have been performed with self-defined car models in virtual scenarios of a public road and an open parking lot and the results are substantial.