Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.

In intelligent driving, situational assessment (SA) is an important technology, which helps to improve the cognitive ability of intelligent vehicles in the environment. Uncertainty analysis is very significant in situation assessment. This article proposes an SA method based on uncertainty risk analysis. Under uncertain conditions, according to the random environment model and Gaussian distribution model, the collision probability between multiple vehicles is estimated by comprehensive trajectory prediction. The proposed method considers collision probabilities of different prediction points within and outside the prediction range and obtains long-term accurate prediction results. The method is suitable for the situation risk assessment of sensor systems in the presence of unexpected dynamic obstacles, sensor failures or communication losses in traffic, and different environmental sensing accuracy. The experimental results show that in the dynamic traffic environment, the proposed scenario assessment method can not only accurately predict and assess the situation risks within the prediction range, but also provide accurate scenario risk assessment outside the prediction range.

Multi-modal fusion can take advantage of the LiDAR and camera to boost the robustness and performance of 3D object detection. However, there are still of great challenges to comprehensively exploit image information and perform accurate diverse feature interaction fusion. In this paper, we proposed a novel multi-modal framework, namely Point-Pixel Fusion for Multi-Modal 3D Object Detection (PPF-Det). The PPF-Det consists of three submodules, Multi Pixel Perception (MPP), Shared Combined Point Feature Encoder (SCPFE), and Point-Voxel-Wise Triple Attention Fusion (PVW-TAF) to address the above problems. Firstly, MPP can make full use of image semantic information to mitigate the problem of resolution mismatch between point cloud and image. In addition, we proposed SCPFE to preliminary extract point cloud features and point-pixel features simultaneously reducing time-consuming on 3D space. Lastly, we proposed a fine alignment fusion strategy PVW-TAF to generate multi-level voxel-fused features based on attention mechanism. Extensive experiments on KITTI benchmarks, conducted on September 24, 2023, demonstrate that our method shows excellent performance.

Intellectualisation is one of the three reforming technologies in automotive industry, which is now changing the mobility mode and human society. High safety and intelligence are the pre‐requisites for putting self‐driving vehicles into markets. This study presents the hardware and software architecture for intelligent vehicles, as well as their road verification in typical traffic scenarios. The hardware system includes environmental sensors, computing platforms, vehicle actuators, and vehicle platforms, which is able to provide redundant protection against the main controller failure. The software system includes environmental perception module, scene cognition module, decision and control module, human–computer interaction module and public service support module. To evaluate the performance of the developed architecture, the road tests of automated driving system were carried out in two typical traffic scenarios, including: (i) closed road test in Yuanboyuan region; (ii) open road test on Beijing‐Tianjin highway. The real road test shows that the designed hardware and software systems for intelligent vehicles have desirable robustness, which can realise accurate and reliable environment perception, decision‐making and motion control.

Purpose: Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach: This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings: The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value: The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.