Lane-changing Process Research Articles

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

A Dynamic Lane-Changing Trajectory Planning Algorithm for Intelligent Connected Vehicles Based on Modified Driving Risk Field Model

A dynamic LC trajectory planning algorithm based on the modified driving risk field is proposed to address the issue of dynamic changes during the lane-changing (LC) process. First, a modified driving risk field (MDRF) model is constructed for LC scenarios. Then, according to the state of the target vehicle and discrete sampling points, a series of LC candidate trajectories were generated based on the quintic polynomial. After eliminating candidate trajectories that do not meet the constraints, the MDRF was utilized as a safety evaluation function. Additionally, comfort and smoothness evaluation functions were combined to evaluate candidate LC trajectories in order to obtain the optimal LC reference trajectory. Then, this paper proposes a dynamic LC trajectory planning algorithm, addressing the challenges of complex traffic scenarios and dynamic changes in adjacent vehicle states. Utilizing the optimal reference trajectory as a basis, a dynamic segmented algorithm is applied to the x–t and y–x curves, constructing an optimized objective function that considers the MDRF. Under multiple constraints, including the continuity and smoothness of the lateral and longitudinal trajectories, the penalty function approach is employed to solve the optimization objective function, yielding the optimal LC trajectory adapted to real-time changes in the traffic state. Finally, the proposed dynamic LC trajectory planning algorithm was validated under four different scenarios by using MATLAB 2023b. The simulation results indicate the safety, continuity, and dynamic feasibility of the proposed algorithm. Moreover, it demonstrates strong adaptability and flexibility in challenging dynamic LC scenarios.

Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach

The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability.

An uncertainty cognition-based game model for lane-changing process in mixed driving environment

The non-instantaneous nature of lane-changing demands real-time adaptability for autonomous vehicles (AVs) to respond continuously changing traffic conditions. In the mixed environment where AVs coexist with human-driven vehicles (HVs), the lack of inter-vehicle information exchange necessitates the Nash Equilibrium as best response. In addition, the unpredictable intentions of HV introduce uncertainty, posing a challenge for the solution of equilibrium. This paper introduces an aggressiveness parameter reflecting human drivers' yielding tendencies to autonomous vehicles and enables human-like uncertainty cognition during lane changes. To meet the practical solution requirements of the uncertainty cognition-based game model, we propose Proactive Equilibrium Strategy Algorithm (PESA) based on two-stage Nash equilibrium and anticipation of the opponent's next-stage strategy. Utilising Next Generation Simulation (NGSIM) as environmental data, PESA shows safer and more efficient lane-changing behaviour and leads to more favourable post-lane-changing traffic conditions compared to actual data outcomes.

Game theory-based mandatory lane change model in intelligent connected vehicles environment

In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions.

Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction

Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles.

Dynamic Planning of Optimally Safe Lane-change Trajectory for Autonomous Driving on Multi-lane Highways Using a Fuzzy Logic–based Collision Estimator

The collision avoidance system in an autonomous vehicle, intended to address traffic safety issues, has a crucial function called collision estimation. It accomplishes this by identifying potential dangers and notifying the drivers in advance or by using autonomous control to navigate safely. In this work, a novel approach is proposed for generating and selecting a lane-change trajectory for the vehicle in a driving scenario where two vehicles are simultaneously executing lane-change processes on highways and approaching the same target lane. Moreover, a novel fuzzy logic estimator based on time-to-collision (TTC) and time-to-gap (TTG) is designed to estimate the collision risk. In the collision-avoidance process, the proposed estimator is utilized to determine the risk of a collision with polynomial function-based generation of possible lane-change trajectories. The safest lane-change trajectory is then provided to the motion controller so it can navigate the vehicle safely through such a challenging lane-change scenario. This work also investigates Stanley and Pure Pursuit controllers to follow the optimized trajectory. The simulation experiment results demonstrate that the proposed approach for dynamic trajectory generation during the lane-change process can successfully handle this type of challenging situation and prevent a potential collision. Experimental results also indicate that monitoring the movement of the nearby lane-changing vehicle is crucial for safe lane-change execution and that the proposed approach successfully handles the challenging situation, preventing potential collision.

Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow

The platooning management technology for Connected Automated Vehicles (CAVs) can potentially increase the efficiency of the traffic system. However, the randomness in the spatial distribution of CAVs poses a new challenge for CAVs’ platooning strategy in mixed traffic flow. To effectively utilize the advantages of platooning technology, this paper proposes a platoon-aware cooperative lane-changing (PCLC) strategy inspired by platoon formation. This strategy focuses on the formation of CAV platoons in mixed traffic flow and restructures the spatial distribution of CAVs on the road segment. First, the cooperative lane-changing behavior of CAVs is modeled based on vehicle-to-vehicle communication, and a gap suitable for lane-changing is created by controlling the acceleration of cooperative CAVs to complete the lane-changing process smoothly. Meanwhile, considering the maximum platoon size, a decision-making framework of cooperative lane-changing for CAVs is designed. Then, considering the change in spatial distribution of CAVs (i.e., platoon intensity) caused by PCLC, the impact of PCLC on traffic capacity is analyzed from theoretical and numerical verification perspectives. Finally, simulation experiments are conducted to investigate the impact of PCLC on lane-changing indicators, traffic flow stability, and traffic efficiency. The simulation experiments show that compared with the discretionary lane-changing (DLC) operation, the PCLC strategy may reduce the lane-changing rate and raise the average platoon size on road segments, increasing road capacity. In addition, the vehicles on the road segment have modest swings in average speed and acceleration, proving the safety and stability of the proposed strategy. Particularly, scenarios with moderate to heavy traffic flow and fairly balanced CAVs penetration rates favor the effectiveness of this strategy. With 50% CAVs and moderate traffic density, the lane capacity increases by 450 veh/h and improves by 10.135%. These findings support the ability of PCLC strategy to reduce traffic congestion and effectively explore traffic management strategies for mixed traffic flow.

Vehicle Driving Behavior Analysis and Unified Modeling in Urban Road Scenarios

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems.

Exploring HDV Driver–CAV Interaction in Mixed Traffic: A Two-Step Method Integrating Latent Profile Analysis and Multinomial Logit Model

Human-driven vehicles (HDVs) will share the road with connected autonomous vehicles (CAVs) in the near future. Accordingly, the investigation of the interactive behavior of HDV drivers toward CAVs is becoming critical. In this study, a questionnaire survey was first conducted. The heterogenous clusters of HDV drivers were revealed through the latent profile analysis based on the collected dataset, with the focus on their trust and familiarity with CAVs, their attitudes towards sharing the road with CAVs, and their risk perception and perceived behavior control when they faced the CAVs. Subsequently, the correlation between the respective latent cluster and several socio-demographic factors was understood based on the multinomial logistic regression model, and the choice behavior of each cluster in different interactive driving scenarios was revealed. Three vital findings were reported. (1) Three profile clusters of HDV drivers (i.e., negative individuals, neutral individuals, and positive individuals) were revealed. (2) The drivers of a low/middle income and with a long driving experience were more likely to be negative individuals, whereas the CAV experience can make drivers feel positive towards CAVs. (3) Negative individuals might give up on changing lanes when a CAV platoon driving was noticed in the target lanes; in addition, they might raise more rigorous requirements for vehicle spacing in the lane-changing process when finding CAVs driving in the target lanes. To be specific, negative and neutral individuals preferred driving in front of the CAV platoons. The findings can provide references for developing effective management measures or CAV control strategies for transportation systems.

Infrastructure-Assisted cooperative driving and intersection management in mixed traffic conditions

Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can’t be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework.

Traffic behavior analysis of the urban expressway ramp based on continuous cellular automata

Urban expressway is an essential part of the urban transportation network, and its role is to speed up traffic operation and relieve traffic congestion. To investigate the complex traffic flow phenomena at urban expressway ramps, we use the Continuous Cellular Automata (CCA) model to model and analyze the urban expressway with entrance and exit ramps in a unified manner. Considering the vehicle's merging, diverging and following behaviors on urban expressway with an entrance and exit ramp, we design the vehicle's main road merging rules, ramp inflow and outflow rules, Cooperative Car-Following for Lane Change process analysis (LC-CCF) model, and improved Nagatani lane change rules to control the overall road vehicle operation. Based on this, we propose a CCA model for urban expressway ramp traffic flow. To clarify the model, The Chengdu Second Ring Elevated Road with entrance and exit ramps as a scenario is used for experiments. The results show that: (1) The spatio-temporal diagrams show a significant metastable congestion phenomenon and distinct congestion boundaries primarily located near the merge and divergence area. Congestion tends to start at the merge and divergence area and then spread outward. (2) The ramp disturbance has an increasingly pronounced effect on average speed and density as Pin−main and Pin−ramp increases. (3) The density-lane change frequency relationship tends to increase and then decrease, with an increase in the frequency of lane changes increasing the interference between vehicles in different lanes and affecting the average traffic speed. The lane change frequency is the main reason for the differences in traffic flow between lanes. (4) The CCA model can effectively characterize congested traffic flows on urban expressway ramps.

Speed limit effect during lane change in a two-lane lattice model under V2X environment

Speed limit measures are ubiquitous due to the complexity of the road environment, which can be supplied with the help of vehicle to everything (V2X) communication technology. Therefore, the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment. Accordingly, the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis. Moreover, the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.

Jamming transition in two-lane lattice model integrating the deception attacks on influx during the lane-changing process under vehicle to everything environment

In the information area of actual traffic, the deception attack can affect the lane-changing rate of influx during the lane-changing process on two lanes operating in a vehicle to everything environment. To counteract the deception attack, a novel lattice model integrating the driver anticipation effect (DAE) will be constructed based on the lane-changing influx rate caused by the deception attack on two lanes. The influence of the lane-changing rate of deceitful influx on traffic stability is investigated through theoretical analysis, and the corresponding modified Korteweg De Vries equation is derived. Using numerical simulation, the evolution of density waves and hysteresis loop phenomena caused by the lane-changing rate of deceitful influx are studied. Due to the rate at which deceitful influx changes lanes, the deception attack on influx will also have a significant impact on the energy consumption of the transportation system.

A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level

As an important driving behavior, lane-changing has a great impact on the safety and efficiency of traffic flow interacting with surrounding vehicles, especially in mixed traffic flows with autonomous vehicles and human-driven vehicles. This study proposes a deep reinforcement learning-based lane-changing model to train autonomous vehicles to complete lane-changing in the interaction with different human driving behaviors. First, a mixed-flow lane-changing environment of vehicle group level is constructed with surrounding vehicle trajectories extracted from natural driving trajectories. Then, the state space and action space are determined, the reward function is designed to comprehensively consider safety and efficiency, so as to guide autonomous vehicles not to collide, and determine the acceleration and direction angle to complete lane-changing behavior, and a collision avoidance strategy is integrated into the proposed method to ensure the safety of longitudinal motion. Furthermore, the trained model can learn the experience of successful lane-changing, resulting in a 90% success rate without collision in testing. Finally, the driving performance of the proposed method is analyzed in terms of safety and efficiency evaluation indicators, which proves that the proposed method can improve the efficiency and safety of the lane-changing process.

A Causal Inference–Based Speed Control Framework for Discretionary Lane-Changing Processes

Vehicle lane changing is one of the most important topics in autonomous driving and is the behavior most likely to cause vehicle accidents. In order to optimize the performance of vehicle lane change by controlling the speed of the vehicle, which makes the lane change safer, more efficient, and more eco-friendly, a causal inference-based speed control framework is proposed in this study. First, variables affecting the lane change efficiency are selected and their treatment effects are obtained by conducting causal effect analysis and causal refutation analysis. Second, the conditional average treatment effect is estimated by taking the safety and eco-friendly requirements into consideration and applying the double machine learning version causal forest model. Finally, a controller is built, and grid search is used to optimize lane change decision-making. By conducting numerical experiments, the average lane change duration decreased by 5.9% and average velocity increased from 7.165 m/s to 7.791 m/s. The results show that the framework has good performance on improving the efficiency and environmental friendliness of a lane change process while ensuring safety. The proposed framework provides a new approach to speed control for discretionary lane-changing behavior. The approach can be well adapted to a vehicle-to-vehicle communication environment and is expected to be applied to vehicle-assisted driving systems in the future.

Lane-changing control for hybrid electric vehicles with dedicated hybrid transmission based on robust model predictive control

Car-following and lane-changing are extremely important for vehicles. The traditional adaptive cruise control (ACC) strategy has various drawbacks due to the complicated driving conditions. A new control strategy of robust model predictive control (RMPC) for car-following and lane-changing is proposed based on a new type of hybrid electric vehicle (HEV) with dedicated hybrid transmission (DHT-HEV). The control model includes a vehicle model, a following and lane-changing model and an RMPC controller. The vehicle model integrates the DHT-HEV and dynamic models with a longitudinal dynamic model, seven degrees of freedom (DOF) vehicle dynamic model and three DOF control model. The following and lane-changing model is described as car-following and lane-changing models of two-quintic function. The RMPC controller is used to balance the control of vehicle longitudinal and lateral dynamics with the lateral acceleration disturbance. Simulation results demonstrate that the RMPC controller can track the reference trajectory during two lane-changing process when the test scenarios of different accelerations are set compared with the conventional ACC control. Different Np (predictive horizon) and Nc (control horizon) are also analysed to improve the solving performance of RMPC. Results indicate that the solving performance of the system is optimal when Np = 30 and Nc = 1. Furthermore, different weight coefficient matrixes ( Q and R) are taken into the consideration by coordinating the car-following performance and lateral stability. Accordingly, the controllers of RMPC-LC, RMPC-ACC and RMPC-LA are set, demonstrating that the control of RMPC-LA best balances two aspects and is always within a safe car-following distance. Meanwhile, the hardware in the loop (HIL) is utilised to verify the effectiveness of the proposed RMPC control strategy, which comprises model compilation, the establishment connection between the D2P ECU and the simulator and control input.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles (CAVs) rely heavily on intelligent algorithms and remote sensors. If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication, it can cause significant damage to CAVs or passengers. The primary objective of this study is to model cyber-attacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment. Based on the analysis on environmental perception system and possible cyber-attacks on sensors, a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed. The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lane-changing process is then quantitatively analyzed. The evaluation indexes include spatio-temporal evolution of average speed, spatial distribution of selected lane-changing gaps, lane-changing rate distribution, lane-changing preparation search time, efficiency and safety. Finally, the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack. Also, when the traffic system is under cyber-attack, more unsafe back gaps are chosen for lane-changing, especially in the center lane. Therefore, more lane-changing maneuvers are concentrated on approaching the off-ramp, causing severe congestions and potential rear-end collisions. In addition, as the number of cyber-attacked vehicles and the severity of cyber-attacks increase, the road capacity and safety level will rapidly decrease. The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

A Hierarchical Motion Planning System for Driving in Changing Environments: Framework, Algorithms, and Verifications

In this article, a hierarchical real-time motion planning system is proposed to solve complex navigation problem in realistic dynamic traffic environments. First, a longitudinal safety spacing model is established to describe the possible collision risk in the lane-changing (LC) process, and a piecewise linearization method is proposed to convert the nonlinear constraints into linear constraints. Second, a decision-making strategy is proposed to decide whether the trajectory should be replanned according to the real-time prediction of the surrounding environments, and select the optimal lane. Third, the quintic B-spline curve method and the quadratic programming method are integrated to obtain the optimal LC trajectory, considering factors of safety, comfort, and efficiency. The terminal position objective function based on the artificial potential field is designed to soften the hard security constraints and guide the LC trajectory toward the center of safety zone. Finally, a model predictive control method based on the kinematics vehicle model is utilized to track the planned trajectory. The experimental results of the miniature intelligent vehicle group verify the real-time performance and effectiveness of the proposed motion planning system.

A centralized relaxation strategy for cooperative lane change in a connected environment

This paper proposes a centralized relaxation strategy for cooperative lane changes on the freeway with a connected and automated vehicle (CAV) dedicated lane. Three numerical models are designed in this scenario. Firstly, a lane-changing decision (LCD) model is established considering the speed difference between merging CAV and the target platoon, and the number of following vehicles (FVs) in the platoon. The merging gap is decided by the gap choice model, which is based on the safe distance according to the Gipps model. The relaxation model controls the longitudinal accelerations of all FVs in the platoon to provide proper gaps for merging CAVs. The parameters of these three models are obtained by sensitivity analysis. The results show that safety risks are significantly reduced in the lane-changing (LC) process with centralized relaxation. Benefitting from extending relaxation time, the decrease in amplitude of acceleration alleviates the speed oscillation among the FVs. It also helps to improve traffic efficiency and driving comfort. However, these advantages are weakened when the number of FVs exceeds 5 or the speed difference is over 7 m/s. It is suggested that the single CAV (SV) should merge to the end of the platoon in these conditions from a systematic view.