A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication

Zhang, Ronghui; Li, Kening; Wu, Yueying; Zhao, Dezong; Lv, Zhihan; Li, Fuliang; Chen, Xia; Qiu, Tony Z.; Yu, Fan

doi:10.1109/tvt.2021.3132558

Cited by 27 publications

(7 citation statements)

References 49 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On-board sensors collect information about the surrounding environment, use wireless communication networks to share their own environmental information with other vehicles, and realize collaborative perception through grid map integration. A large number of scholars at home and abroad have demonstrated through research that in various traffic scenarios, multi vehicle coordination can improve driving safety and efficiency compared to single vehicle path planning methods [29,30].…”

Section: Information Transmission Methods Based On Road Segment Divisionmentioning

confidence: 99%

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

Sun,

Jing,

Xiao

et al. 2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

To eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning.

show abstract

Section: Information Transmission Methods Based On Road Segment Divisionmentioning

confidence: 99%

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

Sun,

Jing,

Xiao

et al. 2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…( , ) X Y . [10] The calculation steps of collision risk and self-vehicle lane determination are as follows Step 1:Combined with the radius of curvature R, the center coordinate 0 0 ( , )…”

Section: Fig5 V2x-aebs Curve Road Scenario Designmentioning

confidence: 99%

Design of advanced emergency braking system based on vehicle-road cooperation

cao,

Liu,

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

“…The longitudinal safety dis tance, often determined using TTC (time-to-collision) [31], serves as a measure for calcu lating the collision safety distance and assessing the safety level. This distance is influ enced by the relative longitudinal velocity between the intelligent driving vehicle and the obstacle ahead [32][33][34]. Thus, the longitudinal safety distance between the vehicle's real time spatial position and the forward obstacle's position can be denoted as 𝑑 , and its value can be derived from Equation (1).…”

Section: Determination Of Control Points For Local Obstacle Avoidance...mentioning

confidence: 99%

Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception

Yang,

Wu,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers.

show abstract

A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication

Cited by 27 publications

References 49 publications

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

Research on a local path planning algorithm based on multivehicle collaborative mapping and a potential field method

Design of advanced emergency braking system based on vehicle-road cooperation

Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception

Contact Info

Product

Resources

About