Optimal Lane Research Articles

Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city.

Dynamic optimal lane management strategy for multi-lane urban expressway with bi-class connected vehicles

Traffic flow mobility on expressway plays an important role in urban development. With the emergent technologies, connected vehicles, including both connected automated vehicles (CAVs) and connected regular vehicles (CRVs), are equipped with connectivity features to improve efficiency of urban expressway in a mixed traffic scenario. Existing research indicates that without targeted management, the integration of CAVs and CRVs into regular vehicles (RVs) traffic can lead to a series of congestion issues. A potential solution lies in the implementation of dedicated lanes, each designated for specific vehicle types, which could alleviate traffic flow complications. Therefore, this paper proposes a dynamic optimal lane management strategy for multi-lane mixed traffic urban expressway, aimed at maximizing the whole discharge flow. To begin with, we present an analytical method to mathematically derive discharge flow of each lane type including mixed traffic lane, CAV/CRV dedicated lane, and RV dedicated lane, from the perspective of both traffic demand and traffic capacity. Then, taking a three-lane mixed traffic urban expressway as an application, we conduct a numerical analysis of the dynamic optimal lane management strategy, based on comparisons among eight distinct strategies, under varying factors of traffic demands, penetration rates of CAVs and CRVs, and allocation proportions of vehicles upstream assigned to mixed traffic lanes. The results validate the effectiveness of the analytical method proposed for lane management strategies. It also indicates that the aforementioned system factors have significant impacts on the dynamic optimal lane management strategy.

Trajectory Planning and Tracking Control for Single Lane Changing with Different Driving Styles of Intelligent Vehicles Based on Seventh-Degree Polynomial

<div>Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that provides the best comprehensive performance under given constraints (initial speed of 20 m/s, lane width of 3.5 m, and a longitudinal distance of 50 m to the obstacle in front), the four-wheel steering model predictive control can effectively track the planned trajectory, with the maximum jerk value being 6.4 m/s<sup>3</sup> and the longitudinal speed after lane change being approximately 12.6 m/s. Although this study assumes specific longitudinal displacement before and after the lane change, the methodology is applicable to other scenarios. For example, it can determine the shortest longitudinal displacement and the optimal lane change trajectory given predefined vehicle speeds and maximum lateral acceleration conditions. The lane change trajectories developed in this study can be directly applied to the system design of autonomous vehicles.</div>

Optimal Lane Management Model for Mixed Traffic Flow With Connected Automated Vehicles on Freeways

Optimal Lane Management Model for Mixed Traffic Flow With Connected Automated Vehicles on Freeways

A stochastic analysis of the dynamic space utilization in warehouses

Improving the volume space utilization of modern warehouses is a critical objective in warehouse layout design, making the accurate estimation of space utilization across different layouts essential. However, the dynamic demand and replenishment processes of heterogeneous stock keeping units (SKUs) in modern warehouses present significant challenges in accurately assessing space utilization. Traditional models have relied on deterministic frameworks that assume constant demand and production processes, which do not fully capture the heterogeneity and stochasticity in demand arrivals, sizes, and replenishment times encountered in practical scenarios. This study aims to develop a framework for computing volume space utilization in modern block stacking warehouses, incorporating both heterogeneity and stochasticity. We investigate two models in warehouse scenarios: the lost sale model, where excess demand is lost, and the backorder model, where unmet demand is backlogged. For each model, we derive the closed-form expressions of three key types of space wastes that characterize the space utilization of warehouses: honeycombing, aisle, and top-of-lane space wastes, using a continuous-time Markov chain analytical framework. Utilizing these expressions, we analyze the tradeoffs among these wastes and identify the optimal lane depth that maximizes space utilization. Our case studies show that, in a stochastic environment, the proposed computational framework allows for a more accurate estimation of space utilization, and the application of the obtained optimal lane depth can achieve significantly higher space utilization than deterministic methods found in the literature. This underscores the importance of incorporating stochasticity and heterogeneity of demand and replenishment in layout design. Additionally, by investigating the sensitivity of space utilization to varying demand processes and replenishment strategies, we provide managerial insights for adapting warehouse layout designs in response to changes in SKUs' demand and replenishment patterns.

Selective State DOT Lane Width Standards and Guidelines to Reduce Speeds and Improve Safety

This research investigates the lane width standards and guidelines implemented by various State Departments of Transportation (DOTs) to reduce vehicle speeds and enhance road safety. Lane width reduction is often perceived as a strategy to mitigate speed and improve safety. Still, its effectiveness and implications vary across different contexts, including regions, urban/rural settings, or other geometric design features. Drawing from interviews with five State DOTs and a review of their road design manuals, this study aims to identify suggested lane widths depending on the contexts, design exception process when narrowing or widening lane widths, and introduce representative before/after studies. The findings indicate that State DOTs tend to have lower recommended lane widths in urban areas than in rural areas. Moreover, lane width standards among these states vary due to several factors, including the geographical location of roadways (urban or rural areas), design or posted speeds, traffic volume, road classification, and geometric road design features. Design exceptions are required if the existing or proposed design element is incompatible with both AASHTO and department governing criteria. In conclusion, the findings will provide valuable insights and recommendations for policymakers, transportation planners, and road engineers to inform optimal lane width and decision-making processes.

Multimodal Fusion Network for 3-D Lane Detection.

3-D lane detection is a challenging task due to the diversity of lanes, occlusion, dazzle light, and so on. Traditional methods usually use highly specialized handcrafted features and carefully designed postprocessing to detect them. However, these methods are based on strong assumptions and single modal so that they are easily scalable and have poor performance. In this article, a multimodal fusion network (MFNet) is proposed through using multihead nonlocal attention and feature pyramid for 3-D lane detection. It includes three parts: multihead deformable transformation (MDT) module, multidirectional attention feature pyramid fusion (MA-FPF) module, and top-view lane prediction (TLP) ones. First, MDT is presented to learn and mine multimodal features from RGB images, depth maps, and point cloud data (PCD) for achieving optimal lane feature extraction. Then, MA-FPF is designed to fuse multiscale features for presenting the vanish of lane features as the network deepens. Finally, TLP is developed to estimate 3-D lanes and predict their position. Experimental results on the 3-D lane synthetic and ONCE-3DLanes datasets demonstrate that the performance of the proposed MFNet outperforms the state-of-the-art methods in both qualitative and quantitative analyses and visual comparisons.

A platoon formation algorithm for intersections with blue phase control in mixed traffic

Increasing attention is being paid to intersection signal control with cooperative platoons. Assuming platoons being formed, such platoons cannot only improve the intersection capacity but also minimize the number of control units, especially when dedicated connected and automated vehicle (CAV) lanes are considered. However, the platoon formation process is often neglected, especially for lane-changing and overtaking maneuvers in mixed traffic. This may jeopardize the potential of signal control with platoons. This article proposes a platoon formation algorithm that computes the optimal lane, platoon sequence, and speed profiles of CAVs under the requirement of the central traffic controller. The algorithm is designed for mixed traffic conditions and hence the performance of human-driven vehicles is also considered. A mixed integer linear program model is formulated to minimize the deviation from the desired platoon configuration and the disturbance to overall traffic under any arbitrary initial condition. Numerical experiments are designed to test the effectiveness and the computational performance of the proposed algorithm. Results show that CAVs with signal control can form platoons with rational motion. Besides, the platoon penetration significantly affects platooning feasibility, while the platoon length does not. This suggests that CAVs can form long platoons at intersections to improve traffic throughput.

Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles

Autonomous driving technology has the potential to alter the way we travel and is rapidly evolving. Sharing rides in autonomous vehicles may become a popular mode of public transportation in the future. One strategy to enhance operational efficiency is to deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs). This study investigated optimal allocation for dedicated AV lanes considering mixed SAVs and regular vehicles (RVs) flows. First, the standard bottleneck model for morning rush hour was extended to account for both ridesharing and automation, and user equilibrium (UE) solutions were obtained by assuming dedicated AV lanes co-exist with general-purpose (GP) lanes. The equilibrium capacity allocation for dedicated AV lanes was determined for multi-modal traffic scenarios. The study found that commuters may prefer to use SAVs because SAVs were more cost-effective and time-saving. Furthermore, analysis of the fixed payment for ridesharing in SAVs revealed that the system performance could be negatively impacted by high fixed payments. The study proposed an enumeration algorithm for developing lane strategies for discrete lane settings. It was found that the deployment of dedicated AV lanes should be matched to the penetration rate of SAVs and different management objectives, following a gradual rather than a radical process. Finally, a sensitivity analysis was performed considering the value of travel time for SAVs and the bottleneck capacity of a dedicated AV lane. This work provides a new solution to AV lane capacity allocation and offers theoretical support for managers to deploy dedicated AV lane allocation.

Optimal lane management policy for connected automated vehicles in mixed traffic flow

The management of lanes for mixed traffic flow consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) can effectively improve the operational efficiency of highways. This paper proposes an optimal lane management policy for connected automated vehicles in mixed traffic flow on highways. Firstly, the functioning characteristics of the mixed traffic flow are analyzed in three aspects, including car-following mode, headway, and platoon size. Secondly, the vehicle probability distributions under different lane management strategies are derived based on the Markov chain and conditional probability. Then, a calculation model for the capacity of mixed traffic flow was constructed by considering the features of lane management strategies, and sensitivity analyses were performed on the model parameters. Finally, a joint optimization model of CAVs dedicated lanes is proposed with the objective of maximum throughput based on constraints such as lane operation strategies, vehicle type ratios, and road resources. The numerical experiments lead to the following conclusions. (1) For two-lane highways, when the traffic demand is less than 6000 veh/h, the mixed driving lane management strategy should be applied; and when the traffic demand reaches 8000 veh/h, and the CAVs penetration rate is more than 0.56, one CAVs dedicated lane is required. Also, the higher the traffic demand, the lower the critical penetration rate of CAVs. (2) In the three-lane highway scenario, no dedicated lane is required when traffic demand is less than 8000 veh/h; one CAVs dedicated lane is needed when demand exceeds 8000 veh/h with CAVs penetration rate greater than 0.38; and two CAVs dedicated lanes are necessary when demand increases to 12800 veh/h and CAVs penetration rate is more than 0.79. (3) As for four-lane highways, the minimum traffic demand for installing one dedicated lane is 15600 veh/h, and the critical penetration rates for one, two, and three dedicated lanes are 0.32, 0.71, and 0.96, respectively. (4) The increase of CAVs dedicated lane usage willingness and platoon size promote the throughput, and the former CAVs dedicated lane usage willingness works more obviously. These research conclusions can provide theoretical and applied practical support for CAVs dedicated lanes management in the future.

An optimal lane configuration management scheme for a mixed traffic freeway with connected vehicle platoons

With the emergent connectivity, both Connected Automated Vehicles (CAVs) and Connected Regular Vehicles (CRVs) have the capability to form platoons that can be defined as connected vehicle platoons, thereby enhancing the capacity of freeways. To fully leverage the innovations of CAVs and CRVs in improving traffic flow mobility, dedicated lanes with distinct vehicle type authority are proposed. Existing studies suggest that the mobility performance of a road section with different lane authority configurations is significantly influenced by the compatibility between lane configurations and traffic flow characteristics, such as penetration rate of connected vehicles (i.e. CAVs and CRVs) and arrival rate per lane. Motivated by the above research need, this paper proposes an optimal lane configuration management scheme to maximize discharge flow in a two-lane freeway mixed traffic environment. To begin with, four lane configuration strategies with different distinct vehicle type authorities are introduced. Analytical models are provided to mathematically derive the discharge flow of given four lane configuration strategies, according to the investigation of single-lane capacity and upstream arriving traffic demand. By configuration strategy-based analytical models, the optimal lane configuration management scheme is proposed. We then conduct numerical analysis to validate the effectiveness of the proposed analytical models of lane configuration strategies under various penetration rates of CAVs and CRVs. As the result indicated, several important factors have significant impacts on the optimal lane configuration management scheme, such as traffic demand and its allocations on each lane, and CAVs/CRVs penetration.

Optimized Flexible Lane Assignment Using Simulation and Metaheuristic Algorithms

Technological advances with connected and autonomous vehicles (CAVs) have opened opportunities to increase the efficiency of transportation networks. A novel control framework called combined flexible lane assignment and reservation-based intersection control (CFLARIC) system has been recently proposed for better management of directionally unrestricted CAVs traffic flows in an urban environment. CFLARIC offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. In CFLARIC, vehicles position themselves in a proper lane before they reach the downstream intersection, which enables resolution of vehicular conflicts both between intersections and within the intersection boxes. Although CFLARIC has shown promising results, only a limited number of lane assignment scenarios have been designed and tested using some preset rules and based on the previous research objectives. Therefore, the optimality of their performance has never been studied. The objective of this study is to address the flexible traffic lane assignment in such a system as a network optimization problem, in which an optimal lane assignment schema is achieved using metaheuristic optimization algorithms. To this end, a combination of NetLogo and the BehaviorSearch tool is utilized for the simulation modeling and optimization process using random search and brute-force as the search algorithms. The output of the optimization process is the lane assignment that leads to a minimum total travel time for a given network geometry and traffic volumes. Results indicate that a flexible control concept such as optimized CFLARIC has great potential to improve the efficiency of traffic control strategies with CAVs.

Lane Change Trajectory Planning Based on Quadratic Programming in Rainy Weather

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s2, the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range.

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.

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions

As an important stage in the development of autonomous driving, mixed traffic conditions, consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), have attracted more and more attention. In fact, the randomness of human-driven vehicles (HDV) is the largest challenge for connected autonomous vehicles (CAV) to make reasonable decisions, especially in lane change scenarios. In this paper, we propose the problem of lane change decisions for CAV in low visibility and mixed traffic conditions for the first time. First, we consider the randomness of HDV in this environment and construct a finite state machine (FSM) model. Then, this study develops a partially observed Markov decision process (POMDP) for describing the problem of lane change. In addition, we use the modified deep deterministic policy gradient (DDPG) to solve the problem and get the optimal lane change decision in this environment. The reward designing takes the comfort, safety and efficiency of the vehicle into account, and the introduction of transfer learning accelerates the adaptation of CAV to the randomness of HDV. Finally, numerical experiments are conducted. The results show that, compared with the original DDPG, the modified DDPG has a faster convergence velocity. The strategy learned by the modified DDPG can complete the lane change in most of the scenarios. The comparison between the modified DDPG and the rule-based decisions indicates that the modified DDPG has a stronger adaptability to this special environment and can grasp more lane change opportunities.

Static Output-Feedback H ∞ Control Design Procedures for Continuous-Time Systems With Different Levels of Model Knowledge

This article suggests a collection of model-based and model-free output-feedback optimal solutions to a general H∞ control design criterion of a continuous-time linear system. The goal is to obtain a static output-feedback controller while the design criterion is formulated with an exponential term, divergent or convergent, depending on the designer's choice. Two offline policy-iteration algorithms are presented first, which form the foundations for a family of online off-policy designs. These algorithms cover all different cases of partial or complete model knowledge and provide the designer with a collection of design alternatives. It is shown that such a design for partial model knowledge can reduce the number of unknown matrices to be solved online. In particular, if the disturbance input matrix of the model is given, off-policy learning can be done with no disturbance excitation. This alternative is useful in situations where a measurable disturbance is not available in the learning phase. The utility of these design procedures is demonstrated for the case of an optimal lane tracking controller of an automated car.

A mixed capacity analysis and lane management model considering platoon size and intensity of CAVs

Traffic flow will be mixed with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) in the future. The randomness of the spatial distribution of different types of vehicles (i.e., CAVs and HDVs) will not be conducive to the stability and safety of traffic flow, leading to the deterioration of traffic capacity. Therefore, reasonable organization and management of the spatial distribution of vehicles in mixed traffic flow are significant for improving the performance of transportation systems. To effectively organize CAVs and realize the management of automated dedicated lanes, this paper proposes a mixed capacity and lane management model considering platoon size and intensity of CAVs. Firstly, the spatial distribution of different headway types is calculated based on a Markov chain model. Secondly, a single-lane capacity model is developed based on the headway distribution. Then, we analyze the sensibility of the model’s parameters, including market penetration rates, platooning intensity, and platoon size of CAVs. Finally, we investigate the relationship between traffic capacity and lane management. Numerical analyses illustrate that the single-lane capacity is improved by increasing the market penetration rate, platoon size, and platooning intensity of CAVs. Moreover, The insight of the lane management model indicates that optimal lane management is associated with the market penetration rate of CAVs. These findings provide a strategy for the operation and management of dedicated lanes of CAVs in the future.

Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms

Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.

Eco-Friendly On-Ramp Merging Strategy for Connected and Automated Vehicles in Heterogeneous Traffic

The on-ramp area usually produces congestion, high energy consumption, and high emission. In order to improve the efficiency of multi-lane heterogeneous on-ramp traffic composed of vehicles with different dynamic characteristics, we propose an eco-friendly on-ramp merging strategy for connected and automated vehicles in heterogeneous traffic. Firstly, the optimal lane decision method is proposed via the conditional proximal policy optimization algorithm to optimize the traffic flow of each lane and avoid local congestion in heterogeneous traffic. Then, considering travel time, energy consumption, and emission, the eco-friendly merging optimization problem is established to optimize vehicles' longitudinal velocity. According to the optimal lane and the optimal longitudinal velocity profile, the lateral end-point and longitudinal end-point of the local trajectory are solved for planning the local trajectory of the vehicles. Each vehicle tracks its local trajectory, realizing eco-friendly on-ramp merging. Compared with two existing ramp merging methods, results show that the proposed algorithm can mitigate local congestion effectively, and has superiority in traffic efficiency, energy economy, and eco-friendliness.