Perform Lane Changes Research Articles

Assessing the impact of driver compliance on traffic flow and safety in work zones amid varied mixed autonomy scenarios

The safety of work zones is a critical issue for drivers, transportation agencies, and governing authorities. In particular, the vehicles that perform lane changes in the proximity of the work zones involving lane closure, pose a significant threat to the safety of the public and the work zone workers, as they need to complete a forced merging. Yet, there is no comprehensive simulation framework to examine the work zone traffic safety under different compliance distributions of the drivers to the warning delivery for the work zone in a mix-autonomy operation of autonomous and human-driven vehicles. To fill this void, we present an integrated microsimulation framework to assess the correlation between the number of vehicles that perform late merge at the taper (LMT) and traffic mobility and safety under different empirical compliance distributions of the drivers to the warning delivery for the downstream work zone.We employ different work zone configurations to illustrate the relationship between late merges at the taper and performance indicators for traffic mobility and safety of the work zone under a variety of work zone configurations. Simulation results show that compliance distribution significantly impacts the number of late merges at the taper (LMTs) and thereby traffic safety and efficiency. Our findings demonstrate that when human-driven vehicles exhibit high compliance behavior to the merging warning signs, it can offset the impact of the lower percentage of market penetration rate (MPR) levels for autonomous operation to achieve comparable traffic safety and efficiency. We further employ the conflation of microsimulation observations and data-driven models to design a regression model to predict LMTs as an indicator for traffic conditions using the work zone configuration as input variables. In particular, we address the heterogeneity induced by the compliance distribution of drivers by sampling the data points from the distribution to capture the diversity in compliance behaviors of the drivers. Our findings can provide insights for practitioners and researchers regarding the optimal compliance distribution using the performance measurements demonstrated in this work.

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

This paper addresses the issue of on-ramp merging in multi-lane freeways and proposes a cooperative control method based on connected and automated vehicles. Focusing on a two-main-lane freeway scenario, the method consists of two key models: a merging sequence decision model and a motion planning model. The merging sequence decision model prioritizes collision avoidance by predicting the motion state and lane-changing trajectory of vehicles in the merging area. The motion planning model utilizes longitudinal and lateral cooperation to control the main lane vehicles to generate the merging gap through coordinated adjustments in the longitudinal speed or by performing lane changes. The optimal merging trajectory is determined using the entropy weight method, and a fast optimization method based on neural networks is employed. Through simulations considering different traffic density combinations, the proposed method is compared with traditional control schemes. Results demonstrate its superiority in improving traffic flow stability, rapidity, and overall efficiency.

Incorporating bus delay minimization in design of signal progression for arterials accommodating heavy mixed-traffic flows

Design of signal progression for arterials accommodating significant transit and passenger car flows is a challenging task, as its effectiveness with state-of-the-art methodologies often degrades with the increasing percentage of bus flows due to the inevitable mutual impedance in the mixed traffic. Conceivably, bus flows, prior to reaching a link’s downstream intersection, may first experience excessive delays incurred by passenger-car queues before arriving or dwelling at a roadside stop. Those dwelling bus flows will in turn constitute a temporary lane-blockage, causing passenger cars, especially those in the rightmost lane, either to perform lane changes to interfere with neighboring traffic or to endure the excessive delay. The recently-emerged innovative design methodologies producing a bus-based signal progression system or dual-modal progression band, despite their effectiveness at the experimental level, are difficult to be supported by responsible agencies in practice unless with the policy mandate of promoting transit usage. Hence, this study proposes an alternative to facilitate bus flows by incorporating the bus delay minimization in the progression design for passenger-car flows, using the optimized offsets, phase sequences, and link progression speed. The proposed model explicitly accounts for all types of bus delays under various traffic scenarios constituting the bus entry time, bus stop location, and the patterns of passenger-car queues. The results from the extensive numerical experiments confirm that the proposed model can generate a bus-friendly signal progression plan for a wide range of mixed bus and passenger car traffic scenarios.

Sparse Road Network Model for Autonomous Navigation Using Clothoids

To autonomously navigate in traffic roads, an Autonomous Vehicle must take into account perception information, as well as the topological and geometric structure of the environment it is inserted in. Specifically in urban scenarios, the vehicle has to plan its path across intersections, roundabouts and perform lane changes to obey traffic rules. In addition, the planning algorithm must also consider the kinematics constraints of the vehicle and comfort parameters to the passengers. This paper proposes a road network model based on clothoids, which embraces the geometric and topological representation of the environment in a compact data structure. Piecewise linear continuous-curvature paths composed of clothoids, circular arcs, and straight lines are used for this purpose. The proposed approaches are evaluated in an urban scenario composed of curved and straight roads with single and double lanes, roundabouts, and intersections. As a result, a navigation architecture for Autonomous Vehicles was developed using the model, including global planning with continuous-curvature paths.

Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.

Outputs bounds for linear systems with repeated input signals: existence, computation and application to vehicle platooning

This paper investigates the effect of repeated time-limited input signals on the output excursion of stable, linear time-invariant systems. It is first shown that the maximum norm of the output signal remains bounded if the repeated input signals are separated by a nonzero dwell time. Then a novel method for computing a tight bound on the output signal norm is proposed. The setting of the paper is motivated by a vehicle platooning application, where vehicles repeatedly open/close gaps in order to perform lane changes. The developed method analyzes driving safety by computing a bound on the spacing error between vehicles when performing repeated open/close gap maneuvers.

Two antennas GPS-aided INS for attitude determination

This paper describes a navigation system incorporating inertial and two-antenna carrier phase differential global positioning system (GPS) measurements. The major objective is to demonstrate full attitude determination even for a nonaccelerating vehicle. The motivating application for this project is vehicle control. Therefore, the system is designed to operate reliably whether or not GPS measurements are available. The navigation system estimates vehicle position, velocity, acceleration, attitude, and angular rates at 150 Hz with accuracy (standard deviation) of 1.5 cm, 0.8 cm/s, 2.2 cm/s/s, 0.03 deg (pitch and roll), 0.1 deg (yaw), and 0.1 deg/s using a 1.01 m baseline between antennas. The navigation state vector is processed to produce a control state vector at 30 Hz. The control demonstrations involve basic trajectory following as well as trajectory relative maneuvering (i.e., tracking sinusoidal perturbations and performing lane changes). During experimental testing, a magnetic system was available to provide a ground truth reference. The demonstrated accuracy is sufficient for control of vehicles in an automated highway system. Other possible applications include precision farming, aircraft and satellite control, and control or guidance of automated mining or highway maintenance vehicles.