## 4 Experiment and Its Simulation Environment
### Vehicle Behavior Control  
In this research, the environmental vehicles are governed by the intelligent driving model (IDM) [25] and the overall braking model (MOBIL) [26]. In addition, this paper uses deep reinforcement learning methods to control the behavior of the autonomous driving vehicle.  
The IDM is a common microscopic model used to control the longitudinal behavior of the vehicle. The model accomplishes vehicle following and collision prevention by assessing the spacing and relative velocity between vehicles. Generally, the longitudinal acceleration in the IDM can be described by Equations (26) and (27).  
\[\overline{v}=a_{\text{max}}\left[1-\left(\frac{v_{c}}{v_{t}}\right)^{\delta}- \left(\frac{d^{*}}{d_{c}}\right)^{2}\right] \tag{26}\]  
\[d^{*}=d_{\text{min}}+Tv_{c}+\frac{v_{c}\Delta v_{c}}{2\sqrt{a_{\text{max}}a_{ \text{min}}}} \tag{27}\]  
The IDM defines vehicle dynamics through the following critical variables: \(\overline{v}\) denotes the current velocity of the subject vehicle, while \(a_{\text{max}}\) specifies the peak acceleration capability. The parameter \(d^{*}\) quantifies the gap distance to the leading vehicle, and \(v_{t}\) indicates the desired cruising speed. A nonlinear velocity regulation coefficient, denoted as \(\delta\), governs speed adaptation responsiveness. Safety constraints are enforced via T, a temporal safety buffer defining the minimum acceptable time-to-collision threshold, and \(\Delta v_{c}\) captures the relative velocity differential between vehicles. The model further incorporates \(d_{\text{min}}\) as the mandatory minimum inter-vehicle spacing and \(a_{\text{min}}\) as the maximum emergency braking capacity. The parameters are configured as follows: the maximum acceleration is 6 m\(\cdot\)s\({}^{-2}\), the speed index parameter is 5, the required time interval T is 1.5 s, the maximum deceleration is \(-5\) m\(\cdot\)s\({}^{-2}\), and the minimum distance is 12 m.  
By contrast, the MOBIL model governs the vehicle's decision to change lanes laterally. This model takes into account the safety and incentive criteria between vehicles to ensure that when changing lanes, the new following vehicle does not cause a collision risk due to sudden deceleration. Within the mobility-oriented lane-changing decision framework, the kinematic response of the target lane's following vehicle is quantitatively evaluated through Equation (28), where the critical parameter \(b_{safe}\) characterizes the maximum emergency braking intensity imposed on the following vehicle during lane changes. To ensure operational safety, the system employs a dual-layer constraint mechanism: Equation (29) rigorously defines the acceleration threshold \(a_{th}\) for safety criteria, which holistically integrates vehicular dynamic constraints and traffic flow stability requirements. This threshold enables optimal decision-making by dynamically balancing traffic throughput efficiency and risk mitigation requirements.  
\[\widetilde{a}_{n}\geq-b_{safe} \tag{28}\]  
\[\underbrace{\widetilde{a}_{e}-a_{e}}_{geovichlet}+p\cdot\left(\underbrace{ a_{n}-a_{n}}_{newfollower}+\underbrace{a_{0}-a_{0}}_{oldfollower}\right)\geq a_{th} \tag{29}\]  
The coordinated velocity optimization mechanism, governed by Equations (28) and (29), ensures systematic speed improvements for the ego vehicle, its immediate follower, and downstream traffic participants in both original and target lanes. Within the MOBIL decision framework, critical operational parameters are calibrated as follows: the social compliance coefficient (\(p=2\times 10^{-4}\)) modulates cooperative interaction intensity between vehicles, while the safety-critical deceleration boundary (\(b_{safe}=2.5\) m\(\cdot\)s\({}^{-2}\)) establishes emergency braking limits to prevent collision risks. Concurrently, the maneuver benefitthreshold (\(a_{th}=0.25\) m\(\cdot\)s\({}^{-2}\)) enforces a minimum acceleration advantage requirement for lane-change authorization.