Trajectory data collected from probe vehicles become increasingly important for urban traffic operation and management. However, current data tend to be sparse in time and space due to technical constraints or privacy concerns, which fail to provide a complete picture of traffic flow. This study proposes a particle filter (PF) based approach to reconstruct the vehicle trajectory for signalized arterial using sparse probe data. First, the arterial intersection is divided into multiple road cells and the estimation of cell travel time is formulated as a quadratic programming problem. Then, PF is applied to reconstruct the incomplete vehicle trajectory between consecutive updates. Specifically, to calculate and update the weight of initial particles, three measurability criteria are designed for importance sampling considering the structure of signalized arterial and the feature of vehicular updates, i.e., travel time adjustment accuracy, arterial link speed limit and travel time adjustment possibility. Last, NGSIM trajectory data are extracted at intervals to construct the sparse data, which are used to verify the effectiveness of the proposed method. The results show that reconstructed trajectories match closely with ground truth both at the single intersection and along the arterial with multiple intersections.

Autonomous driving involves multi-timescale and multi-objective tasks coupled with long-term driving decision and short-term motion planning. However, existing studies tend to investigate them separately or assume they were synchronous and instantaneous, failing to deeply interpret their interconnections in autonomous driving. To address it, this study explored driving decision and motion planning in an integrated manner, and proposed a double-layer reinforcement learning (RL) framework to couple and optimize these two modules. Specifically, on the upper layer of the framework, a trajectory-level reward function associated with safety, efficiency, comfort and decision execution was proposed, and a decision-making model was established based on value function-based deep reinforcement learning (DRL) algorithms. Then, the reward function of the upper layer was taken as the objective function of the lower layer with relevant state constraints. The model predictive control (MPC) method was used to derive the optimal maneuver sequence guided by driving decisions, and the performance evaluation of motion planning was fed back to the decision-making for DRL parameter optimization. Accordingly, a closed-loop mechanism was developed consisting of forward decision-making output guidance and backward motion planning feedback optimization. Then, two-lane and three-lane interactive scenarios were set for framework training and testing. Last, comparative experiments were conducted using NGSIM dataset. The results demonstrated the effectiveness and the enhanced driving performance of the proposed framework in contrast to four benchmarks.

Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions.