The complex and variable mountainous area causes the wind field to exhibit unsteady and non-stationary characteristics, posing significant challenges to traffic safety at bridge-tunnel connection sections. To study the variation law of the spatial characteristics of the wind field at the bridge-tunnel connection sections in complex mountainous area, this paper takes the mountainous terrain within an 8 km diameter range at the junction of G318 and S217 as the research background. It acquires the digital elevation model (DEM) of the study area’s terrain, and utilizes a reverse fitting method to construct mountain mass models for bridge-tunnel connection sections of five varying lengths. With reference to the standard 16-point wind rose diagram, inflow conditions were configured. The spatial distribution characteristics of wind fields at bridge-tunnel connection sections under various inflow conditions were obtained through numerical simulation. The results show that the error between the numerical simulation results and the on-site measured data is generally within 20%, indicating that the numerical simulation method has high accuracy. Under the condition that the slope of the mountain remains approximately unchanged, affected by the actual complex terrain, cross-bridge wind speeds, vertical wind profiles, and wind attack angles exhibit distinct characteristics at bridge-tunnel connection sections of varying lengths, though they demonstrate similar overall patterns. When the incoming flow is perpendicular to the bridge-tunnel connection section, the wind speed reaches the maximum at the mid-span due to the canyon acceleration effect. This acceleration effect increases as the length of the bridge-tunnel connection section decreases. Under other cases, due to the reduction effect of the high and steep mountains on both sides, the incoming flow decreases and the wind speed is the minimum along the bridge-tunnel connection section. The steep mountainous terrain and river bends significantly influence the vertical cross-bridge wind speed distribution. Within lower-elevation canyons, shorter bridge-tunnel connection sections experience more pronounced effects. Wind attack angles also exhibit substantial terrain-induced variations, predominantly manifesting as negative attack angles overall. The variation laws obtained from the numerical simulation study of the wind field in the bridge-tunnel connection section of complex mountainous areas can provide certain guidance and reference for the study of driving safety at bridge-tunnel connection sections.

To solve the safety problem of collision between a high-speed intelligent vehicle and a low-speed vehicle on the superhighway, the vehicle braking collision avoidance system was studied by using the method of cosimulation. CarSim was used to establish the vehicle dynamics model and set the front vehicle parameters, road parameters and sensor parameters, and the control model based on vehicle distance and speed was established in MATLAB/Simulink. The signal connection is established through the input and output parameter interface module of CarSim software. When the speed of the front car is 100, 120 and 140 km/h, and the speed of an intelligent vehicle is 140, 160 and 180 km/h, respectively, the control model sends the braking deceleration signal to the intelligent vehicle through the real-time distance and speed collected by the sensor and establishes the emergency braking collision avoidance strategy for the vehicle on the superhighway. The results show that when the road adhesion coefficient is 0.60 and the car is braked on the flat straight section of the superhighway, the optimal wheel cylinder pressure is 7 MPa, and at this time, the braking distance of the car is 170.3 m at a speed of 160 km/h. The front car travels at a speed of 100, 120 and 140 km/h, and the smart car brakes at 140, 160 and 180 km/h, respectively, to the same speed as the car in front, requiring relative distances of 10.8, 10.7 and 10.5 m, respectively. When the road adhesion coefficient is 0.60, the vehicle speed of the front vehicle is 100, 120 and 140 km/h, respectively. When the initial cylinder pressure is 1 MPa and the intelligent vehicle braking decelerates to the same speed as the front vehicle, the distance between the front suspension of an intelligent vehicle and the rear suspension of the vehicle in front is 3.1, 3.5, and 3.8 m, respectively. When the initial cylinder pressure is 3 MPa and the intelligent vehicle braking decelerates to the same speed as the front vehicle, the distance between the front suspension of an intelligent vehicle and the rear suspension of the vehicle in front is 7.0, 7.3, and 7.7 m, respectively. Through the CarSim/Simulink co-simulation platform of vehicle emergency braking and collision avoidance control, the validity and accuracy of superhighway braking and collision avoidance model are verified, which can improve the safety of superhighway driving.

To improve the driving safety of autonomous vehicles on superhighways, this paper proposed a lanechanging trajectory planning strategy. Firstly, five polynomials were used to generate general lane-changing trajectory clusters, and the trajectory planning problem was quantified as the duration of solving lane-changing behavior with the limit of vehicle dynamics and surrounding traffic vehicles. Then, considering the constraints of vehicle dynamics, the vehicle dynamics model and Brush tire model were established. Based on the tire lateral force data of the established vehicle model, the tire lateral stiffness was solved, and the magic tire model was used to verify the tire lateral stiffness. Next, the phase plane of sideslip angle and yaw rate was introduced to obtain the safe driving envelope of high-speed vehicle. CarSim simulation training was carried out on given multiple groups of vehicle speeds and adhesion coefficients to determine the shortest lane-changing time that meets the vehicle dynamics constraints. Finally, considering the collision avoidance constraints with surrounding traffic vehicles, three typical lane-changing scenarios were analyzed. The shortest and longest lane-changing durations satisfying the collision avoidance requirements were determined based on the position of single obstacle vehicle, and the threshold model of lane-changing duration satisfying the safe lane-changing requirements was established. The multi-parameter safety lane-changing domain test shows that the established vehicle safety lane-changing duration boundary model can solve the safe and feasible lane-changing trajectory under the given parameters, provide trajectory reference for the superhighway lane-changing behavior, and improve the safety of the superhighway lanechanging behavior.