Influence of Lane Width on Semi- Autonomous Vehicle Performance

ABSTRACTLane and shoulder widths are important highway safety factors because wider lanes and shoulders generally help to reduce crashes. In practice however, due to physical limitations of the right-of-way or statutory restrictions, it may be the case that the existing overall width of the roadway (lanes plus shoulders) cannot exceed a certain value. For a given overall roadway width constraint, wider lanes mean there will be smaller space for shoulders, and vice versa. In such zero-sum situations, it is not certain if a cross-sectional configuration with wide lanes and narrow shoulders is safer compared with one with narrow lanes and wide shoulders. Clearly, for a given total roadway width (TRW), the trade-off between shoulder and lane widths should be evaluated in order to maximize the user benefits (safety) without unduly incurring excessive agency life-cycle costs of construction and preservation. The TRW allocations to the lane and shoulder, expressed as the ratio of lane width to shoulder width, will generally depend on the TRW; lane and shoulder pavement material types and costs; and traffic volume, among other factors. In order to optimize the lane and shoulder width allocations for a given TRW, this paper proposes a framework that formulates the problem as an optimization problem with the objective of minimizing the total (agency and user) life-cycle cost of the roadway. First, the relationships are established between lane/shoulder widths and the resulting user costs (crashes), and also between lane/shoulder widths and the associated agency costs of lane/shoulder construction and preservation. The sensitivity of the optimal solution to different evaluation inputs (such as the relative weights between the agency and user costs) is also analyzed. Using the developed framework, the paper presents a number of decision support charts that can be used by highway agencies to determine the optimal lane and shoulder widths under a given set of conditions including the highway functional class and the total available roadway width. The flexibility of the proposed optimization framework to accommodate risk compensation effects was also demonstrated.

Reducing vehicle lane widths has been proposed as an effective strategy to decrease vehicle speeds and enhance road safety. However, the safety benefits of narrower travel lanes remain a topic of debate due to mixed findings in the literature. This study examines the relationship between lane width, vehicle speed, and crash occurrence to comprehensively understand their impact on road safety and transportation planning. Using data from 320 urban arterial sections in Utah, the analysis reveals that narrower lane widths are associated with reduced vehicle speeds. For every additional foot of lane width, 85th and 95th percentile speeds increase by 1.012 mph and 1.088 mph, respectively. Furthermore, injury crash modeling indicates that a one-foot increase in lane width is associated with a 38.3% increase in the odds of an injury crash on a roadway section. These findings contribute to the growing evidence supporting the implementation of narrower lane widths as a strategy to improve road safety, foster multimodal infrastructure, and promote sustainable urban transportation systems. We recommend that UDOT adopt a minimum lane width of 10 or 11 feet for arterials in highly urbanized areas, such as downtowns and major activity centers.

Lane width evaluation is one of the crucial aspects in road safety inspection, especially in work zones where a narrow lane width can result in a reduced roadway capacity and also, increase the probability of severe accidents. Using mobile mapping systems (MMS) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information along road surface. This paper presents an approach to derive lane width estimates using point clouds acquired from a geometrically-calibrated mobile mapping system. Starting from an accurate LiDAR point cloud, the road surface is extracted with the assistance of trajectory elevation data. Lane markings are identified based on the intensity data. Next, the lane marking centerline is derived and clustered to identify areas with ambiguous or missing lane markings and finally, use the normal (or, unambiguous) lane markings to estimate the lane width. The derived lane width estimates are used to develop a reporting mechanism for areas with narrow lanes, ambiguous lane markings, missing lane markings, and/or wide lanes.

PurposeThere is lack of knowledge about how the existing streets need to be redesigned and the infrastructural changes that need to be made to adopt autonomous vehicles. The purpose of this study is to investigate the infrastructure requirements of autonomous vehicles in terms of (1) lane widths, (2) parking spaces, (3) drop-off zones and (4) other facilities, followed by analyzing them and suggesting changes in the existing urban design of Msheireb Downtown Doha (MDD).Design/methodology/approachMixed method of combining both qualitative (secondary research of analyzing the existing data about the urban design guidelines for an autonomous future, observations of the existing infrastructure) and quantitative methods (on-site measurements of pedestrian walkways and road lane widths) is used.FindingsThe outcome of the research consists of a series of major infrastructural changes with regard to lane widths, parking spaces, pick-up and drop-off zones and other facilities needed for the deployment of autonomous vehicles.Practical implicationsThe results imply that Qatar can benefit by adopting the proposed urban design suggestions for the implementation of autonomous vehicles on the streets of MDD in particular, and smart cities of Qatar and the region in general.Social implicationsThe proposed changes can work as a reference and serve as a possible setting for addressing Autonomous Vehicle preparations in emerging cities.Originality/valueThe proposed urban design changes can be adapted for an autonomous future in emerging cities.

Traffic data were collected from 11 work zones on Interstate highways in Illinois in which one of the two lanes was open. The reductions in free-flow speed (FFS) due to narrow lanes and lateral clearances in work zones were studied. It was found that the reductions in FFSs of vehicles in work zones because of narrow lanes were higher than the reductions given in the Highway Capacity Manual for basic freeway sections. The data also showed that the narrower the lane was, the greater the speed reduction was. The data showed that the FFSs of heavy vehicles were statistically lower than the FFSs of passenger cars, even though the speed limit was the same for both types of vehicles. In addition, the reduction in the FFSs of heavy vehicles was greater than the reduction in the FFSs of passenger cars. This greater reduction in the speed of heavy vehicles affected the performance of the traffic stream in work zones. Thus, it should be considered in the computation of the passenger car equivalence for heavy vehicles. It is recommended that 10, 7, 4.4, and 2.1 mph be used for speed reduction in work zones for lane widths of 10, 10.5, 11, and 11.5 ft, respectively.

The purpose of the study was to determine the cost-effectiveness of widening lanes and shoulders on rural, two-lane roads. Information concerning geometrics, accidents, and traffic volumes was obtained for over 15,00 miles (25,000 miles km) of roads. Reductions in accident rates occurred as lane and shoulder widths increased. Run-off-road and opposite-direction accidents were the primary accident types associated with narrow lanes and shoulders. Reductions in accidents ranged from 10 to 39 percent for lane widening and 6 to 21 percent for shoulder widening. Priority listings were prepared for 531 projects based on critical rate factor. Priority listings were also prepared for the top 120 lane-widening projects and the top 36 shoulder-widening projects based on benefit-cost ratios.

Road infrastructures are fundamental parts of peoples’ lives, allowing them to access various destinations and activities. Accordingly, infrastructure should be in an appropriate condition. A pavement maintenance plan should be optimized, and pavement condition should be predicted accurately to obtain optimal pavement maintenance solutions. Therefore, the prediction of pavement conditions with high accuracy has been an immense concern. This study aims to introduce a new approach to accurately predict pavement international roughness index (IRI) over the long term. To this end, all the vital parameters, including initial IRI, pavement age, lane width, traffic loadings, structural characteristics, climatic features, and pavement distresses, are considered. With all the vital parameters, the prediction problem includes 58 variables. Thus, the application of a proper feature-selection technique is vital. To this end, a novel hybrid feature-selection method is introduced by a combination of arithmetic optimization algorithm and stochastic gradient descent regression (AOA-SGDR). Moreover, the performance of the proposed feature-selection method is compared with Lasso and all features. Five machine-learning algorithms, including random forest regression (RFR), support vector machine, multi-layer perceptron, decision-tree regression, and multiple linear regression, are employed for the prediction process. By employing AOA-SGDR, the average testing-data mean absolute error (MAE) reduces by at least 7.92%. Meanwhile, RFR provides the highest accuracy, with average testing-data MAE of 0.095 m/km. Moreover, analyzing the parameters indicates that initial IRI, pavement age, equivalent single axle load (ESAL), and structural number (SN) have the most significant relative influence on IRI.

A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds

A Decision Support System for Road Safety Analysis

Autonomous vehicles can be operated without the need for human intervention by perceiving their physical environment, thanks to their automatic control systems. At present, extensive studies on autonomous land, sea, and air vehicles are ongoing all around the world. This project is focused on autonomous land vehicles. In the simplest examples, the speed and position of the vehicle can be controlled with the position sensor in the front wheel section and the speed sensors in the rear wheel section. Autonomy is achieved by generating signals for the control system by interpreting the data from various sensors on the vehicle with the aid of an algorithm. Different control methods can be applied for such vehicles. Within the scope of this project, the control of the vehicle will be provided by PID (Proportional-Integral-Derivative) control method, in order to constitute a basis for autonomous vehicle technology,

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping.

Recently vision sensor based lane detection technology is being magnified as automated driving service get into the acceleration step. However, lane detection is not used lateral risk assessment or lateral control alone but also used front target vehicle selection for the longitudinal control system. Ego lane detected from vision sensor is a good assumption as an ego vehicle's driving path and would be useful to front target vehicle decision. In this sense, the importance of curvature and curvature-rate which have been relatively devaluated then lateral offset and heading angle is getting higher. In this study, a novel approach of lane estimator for more robust curvature information is proposed with Kalman filter. The lane estimator is designed with clothoid road model and an assumption based on lane width. To verify the performance of proposed lane estimator, real-car test in the public road is carried out. The test result shows that robustness of the proposed lane estimator even in failure situation of front camera. Furthermore, the result also showed that accuracy and usefulness of lane estimation based target selection. Through the test result analysis, filtered only two coefficients which related clothoid curvature parameters could make lane information more accurately.

Influence of road markings, lane widths and driver behaviour on proximity and speed of vehicles overtaking cyclists

The introduction of new technologies, such as artificial intelligence in cars, raises the question of what the road infrastructure should be like in the future. The changes are expected to be significant, given that the human factor has influenced a number of design parameters. The ability of autonomous vehicles to drive themselves, anticipate situations, communicate with surrounding vehicles and infrastructure, and the environment in which they are located, places new demands on road infrastructure. Gradual changes in infrastructure will mostly depend on the speed of development of autonomous vehicles and their introduction into the transport system. Starting with intensive maintenance of roads and accompanying facilities, through separate corridors only for autonomous vehicles, while the goal is simplified and safe road infrastructure. The aim of this paper is to provide a review of the literature for the possible adaptation of road infrastructure intended for autonomous vehicles and to lay the foundation for further research in this area.

Despite the many advancements in traffic safety, vehicle overtaking still poses significant challenges to both human drivers and autonomous vehicles, especially, how to evaluate the safety of passing a leading vehicle efficiently and reliably on a two-lane road. However, few realistic attempts in this field have been made in the literature to provide practical solutions without prior knowledge of the state of the environment and simplifications of vehicle models. These model simplifications make many of the proposed solutions in the literature unusable in real scenarios. Considering the dangers that can arise from performing a defective overtake and the substantial risk of vehicle crashes during high-speed maneuvers, in this paper we propose a system based on model predictive control to accurately estimate the safety of starting a vehicle overtake in addition to vehicle control during the maneuver that aims to ensure a collision-free overtake using a complete and realistic model of the vehicle’s dynamics. The system relies on a stereoscopic vision approach and machine learning techniques (YOLO and DeepSORT) to gather information about the environment such as lane width, lane center, and distance from neighboring vehicles. Furthermore, we propose a set of scenarios to test the performance of the proposed system based on accurate modeling of the environment under a range of traffic conditions and road architecture. The simulation result shows the high performance of the proposed system in ending collisions during overtaking and providing optimal pathing that minimizes travel time.