Lane Change Control Based on Vehicle Driving Safe Boundary

Wang, Huiran; Wang, Qidong; Chen, Wuwei; Zhao, Lian; Wei, Zhenya; Cai, Bixin

doi:10.3901/jme.2020.18.143

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When j = r, (i, r) represents the first vehicle behind the ego-vehicle in the lane i. According to the relative motion relation between the ego-vehicle and other vehicles, combined with the literature, [12] it can be known that the safety level of the driving area between the ego-vehicle and the vehicle (i, f ) and (i, r) is judged according to Equations ( 1) and ( 2) respectively…”

Section: Deficiencies In System Functional Designmentioning

confidence: 99%

“…Many previous studies on the prediction method of driver behavior only consider the state of the driver and the ego-vehicle. [14][15][16][17] Because the influence of external driving environment factors on the driver is ignored, it is difficult to subdivide the driver takeover state further. In this paper, driver-vehicle state parameters and vehiclevehicle state parameters are considered comprehensively, and multi-classification output prediction is made by the deep neural network (DNN) to judge driver takeover behavior.…”

Section: Takeover Behavior Prediction Based On Dnnmentioning

confidence: 99%

“…When

j\ = \ r

, (

i,r)

represents the first vehicle behind the ego‐vehicle in the lane

i

. According to the relative motion relation between the ego‐vehicle and other vehicles, combined with the literature, [ 12 ] it can be known that the safety level of the driving area between the ego‐vehicle and the vehicle (

i,f)

and (

i,r)

is judged according to Equations (1) and (2) respectively

\begin{equation}\left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{l}@{}} {{T}_{{\mathrm{ttc}}f} \ge {T}_{{\mathrm{s1}}},\ v - {v}_f &gt; 1.5m/s}\\[3pt] {\Delta {y}_f \ge v{T}_{{\mathrm{s2}}},\ v - {v}_f \le 1.5m/s}\\[3pt] {\Delta {y}_f - \ {S}_s = \frac{{{a}_f{T}_{{\mathrm{ttc}}f}^2}}{2}\ + \left( {v - {v}_f} \right){T}_{{\mathrm{ttc}}f}} \end{array} } \right.\ \end{equation}

({, \begin{matrix} T_{ttc r} \geq T_{...} \end{matrix})

…”

Section: Safe Driving Area Definitionmentioning

confidence: 99%

See 2 more Smart Citations

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

Wang,

Chen

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

The driver authority decision for driver‐initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver‐automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems‐theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver‐vehicle state parameters and vehicle–vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver‐automation driver authority decision method is designed to make the driver‐machine take‐over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware‐in‐the‐loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety.

show abstract

Section: Deficiencies In System Functional Designmentioning

confidence: 99%

Section: Takeover Behavior Prediction Based On Dnnmentioning

confidence: 99%

“…When

j\ = \ r

, (

i,r)

represents the first vehicle behind the ego‐vehicle in the lane

i

i,f)

and (

i,r)

is judged according to Equations (1) and (2) respectively

\begin{equation}\left\{ { \def\eqcellsep{&}\begin{array}{@{}*{1}{l}@{}} {{T}_{{\mathrm{ttc}}f} \ge {T}_{{\mathrm{s1}}},\ v - {v}_f &gt; 1.5m/s}\\[3pt] {\Delta {y}_f \ge v{T}_{{\mathrm{s2}}},\ v - {v}_f \le 1.5m/s}\\[3pt] {\Delta {y}_f - \ {S}_s = \frac{{{a}_f{T}_{{\mathrm{ttc}}f}^2}}{2}\ + \left( {v - {v}_f} \right){T}_{{\mathrm{ttc}}f}} \end{array} } \right.\ \end{equation}

({, \begin{matrix} T_{ttc r} \geq T_{...} \end{matrix})

…”

Section: Safe Driving Area Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

Wang,

Chen

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…In the process of lane changing, the vehicle speed, acceleration and yaw rate should also be considered [8]. The trajectory cluster is screened by constraint conditions, and the current optimal lane-changing trajectory is obtained by calculating each trajectory evaluation function in the trajectory cluster.…”

Section: Constraints and Evaluation Functionsmentioning

confidence: 99%

Lane Change Decision and Trajectory Planning for Intelligent Cars in Curved Road Scenarios

Wang

2022

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

In order to improve the efficiency of curved road passage and the safety of lane changing, the curved road section is used as the research object, and the curved road lane changing model is established by analyzing the lane changing decision and trajectory planning of intelligent vehicles. In the lane change decision, the influence of different driving styles, curved road radius, car type and other factors are considered, and the improved car ellipse model is proposed and applied to the minimum safety distance for free lane change of intelligent vehicles. In terms of trajectory planning, different lane change executable regions are proposed for drivers with different driving styles, and the optimal lane change trajectory within the lane change executable region is obtained by filtering the trajectory clusters under the Frenet coordinate system through the evaluation function. MPC control is used for trajectory tracking. Finally, the validity of the intelligent vehicle curved road lane change model is verified through Carsim and Simulink simulation.

show abstract

Design of Autonomous Vehicle Trajectory Tracking System Based on MPC in Unknown Environment

Jiang

et al. 2022

2022 41st Chinese Control Conference (CCC)

View full text Add to dashboard Cite

Lane Change Control Based on Vehicle Driving Safe Boundary

Cited by 3 publications

References 0 publications

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

Lane Change Decision and Trajectory Planning for Intelligent Cars in Curved Road Scenarios

Design of Autonomous Vehicle Trajectory Tracking System Based on MPC in Unknown Environment

Contact Info

Product

Resources

About