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.

Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore the correlation between them, leading to the lack of consideration for interaction and dynamic changes of traffic scenarios. To address this challenge, we propose InteractionNet, which leverages transformer to share global contextual reasoning among all traffic participants to capture interaction and interconnect planning and prediction to achieve joint. Besides, InteractionNet deploys another transformer to help the model pay extra attention to the perceived region containing critical or unseen vehicles. InteractionNet outperforms other baselines in several benchmarks, especially in terms of safety, which benefits from the joint consideration of planning and forecasting. The code will be available at https://github.com/fujiawei0724/InteractionNet.

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.

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.