Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

Key Technologies in Connected Autonomous Electrified Vehicles

The rapid development of technologies such as artificial intelligence,Internet of Things,big data,and information communication has promoted the development of connected autonomous electrified vehicles with functions such as complex environment perception,intelligent decision-making,collaborative control,and execution.The main objective of this special issue aims at bringing scholars to show their latest research results in environmental perception systems.

The status,challenges,and trends:an interpretation of technology roadmap of intelligent and connected vehicles in China(2020)

Purpose–The rapid development of Intelligent and Connected Vehicles(ICVs)has boomed a new round of global technological and industrial revolution in recent decades.The Technology Roadmap of Intelligent and Connected Vehicles(2020)comprehensively analyzes the technical architecture,research status and future trends of ICVs.The methodology that supports the roadmap should get studied.Design/methodology/approach–This paper interprets the roadmap from the aspects of strategic significance,technical content and characteristics of the roadmap,and evaluates the impact of the roadmap on researchers,industries and international strategies.Findings–The technical architecture of ICVs as the“three rows and two columns”structure is studied,the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed.Originality/value–This paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.