ADAS safety impacts on rural and urban highways

As a new approach, intelligent speed adaptation (ISA) systems are being developed to provide drivers with assistance for controlling their driving speeds. In this study, ISA impacts were investigated through micro-simulation. The simulation results show that ISA has positive impacts on safety in terms of reducing speeds and speed variations. Applying ISA can harmonizing vehicle speeds between and within lanes, which results in reductions of the number of lane changes and short time-to-collision events. However, journey times could be increased because of speed decreases. This is part of the work of STARDUST, an EU project aimed at assessing the extent to which advanced driver assistance systems (ADAS) and automated vehicle guidance (AVG) systems can contribute to sustainable urban development. The main objective of this simulation study was to assess what impacts intelligent speed adaptation (ISA) have on traffic performances.

Desirability of advanced driver assistance from road safety perspective: the case of ISA

An ex ante impact assessment of advanced driver assistance systems was conducted with focus on autonomous intelligent cruise control and intelligent speed adaptation. The effects of these systems on efficiency, reliability, driving comfort, and safety were addressed by microsimulation for different penetration levels and bottleneck layouts. The deployment of cruise control improves bottleneck capacity. The bottleneck reliability, however, deteriorates in most cases. No significant changes in traffic safety, expressed by the time drivers are exposed to small time-to-collision values, could be established. From the simulation experiments it appears that intelligent speed adaptation has no effect on capacity and provides no substantial contribution to bottleneck reliability. Contrary to expectations, no significant safety benefits could be established using the study’s assessment approach.

How much benefit does Intelligent Speed Adaptation deliver?—An analysis of its potential contribution to safety and environment

Adapting ISA system warnings to enhance user acceptance

The long-term effect of intelligent speed adaptation on driver behaviour

• The performance of Adaptive Cruise Control (ACC) is limited on road curves. • The Intelligent Speed Adaptation for Visibility (V-ISA) adjusts speed based on road conditions. • A driving simulation study assessed the influence of ACC and V-ISA on driver behaviour. • Integrating ACC and V-ISA significantly improved longitudinal driving performance. • The system achieved high system acceptance with no added mental workload. Advanced Driver Assistance Systems (ADAS) have become an integral part of modern vehicles, significantly changing the driving experience. Adaptive Cruise Control (ACC) assists drivers in maintaining a constant speed and a safe following distance; however, it does not moderate the speed along curves when sight limitations occur, thus compromising safety. To address these challenges, this study integrates the Intelligent Speed Adaptation for Visibility (V-ISA) system with ACC. The V-ISA system dynamically adjusts the vehicle speed based on real-time visibility conditions, thereby enhancing safety on road curves where sight distance is restricted. Additionally, this study evaluates the impact of the ACC + V-ISA system on driver performance, specifically focusing on speed management, lateral vehicle control, and driver experience with assistance systems. The study was conducted in a simulated environment with forty-five participants. The results revealed that the ACC + V-ISA system effectively reduced vehicle speed on curves with limited visibility. The ability of the system to regulate speed according to prevailing road conditions highlights its potential to improve road safety. Moreover, the findings suggest that using the V-ISA system integrated with ACC maintains user acceptance and satisfaction while imposing no additional mental workload compared to using ACC alone. These insights are crucial for the future design of ADAS and autonomous vehicles and emphasize the importance of introducing this system into vehicles. LED bar colour

Intelligent speed adaptation: Preliminary results of on-road study in Penang, Malaysia

Intelligent speed adaptation (ISA) has the potential to improve road traffic safety significantly. Large-scale implementation of ISA, however, is hampered by large uncertainties, such as the level of compliance (LC), reliability of the technology, unexpected costs and so on. We suggest an adaptive policymaking approach for implementing ISA. We propose five steps: (1) specify the policy problem, (2) assemble a basic policy, (3) specify the rest of the policy, (4) learn from real world experience with the basic policy and (5) adjust the implemented policy. We demonstrate, as a proof of concept, how to design a basic ISA policy and how to gain knowledge over time about the behaviour of the system in order to resolve some of the uncertainties and improve the policy. By monitoring the percentage of speeding drivers to detect changes in the LC, the policy can be adapted to unfolding events. Hence, by using the adaptive approach, policymakers can proceed with implementing ISA despite the large uncertainties.

A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger.

Most traffic accidents occur at unsignalized intersections, mainly due to inappropriate behavior of non-priority vehicles. The authors have developed an advanced Intelligent Speed Adaptation (ISA) system with GPS/GIS to provide warning to stop failure vehicles at unsignalized intersections. In this study, experiments related to driver attitudes toward stop sign warning were carried out. At a drivers training course, drivers attitudes were tested using a vehicle equipped with the warning system in order to ascertain appropriate timing of warnings when approaching intersections. The drivers evaluation and willingness-to-pay for the warning system were also analyzed using an interview survey answered by testers.

ROAD SAFETY: A MAJOR PUBLIC HEALTH ISSUE

There is no definite conclusion about what the main variables that play a fundamental role in road safety are. Therefore, the identification of significant factors in road accidents has been a primary concern of the transportation safety research community for many years. Every accident is influenced by multiple variables that, in a given time interval, concur to cause a crash scenario. Information coming from crash reports is very useful in traffic safety research, and several reported crash variables can be analyzed with modern statistical methods to establish whether a classification or clustering of different crash variables is possible. Hence, this study aims to use stochastic techniques for evaluating the role of some variables in accidents with a clustering analysis. The variables that are considered in this paper are light conditions, weekday, average speed, annual average daily traffic, number of vehicles, and type of accident. For this purpose, a combination of particle swarm optimization (PSO) and the genetic algorithm (GA) with the k-means algorithm was used as the machine-learning technique to cluster and evaluate road safety data. According to a multiscale approach, based on a set of data from two years of crash records collected from rural and urban roads in the province of Cosenza, 154 accident cases were accurately investigated and selected for three categories of accident places, including straight, intersection, and other, in each urban and rural network. PSO had a superior performance, with 0.87% accuracy on urban and rural roads in comparison with GA, although the results of GA had an acceptable degree of accuracy. In addition, the results show that, on urban roads, social cost and type of accident had the most and least influence for all accident places, while, on rural roads, although the social cost was the most notable factor for all accident places, the type of accident had the least effect on the straight sections and curves, and the number of vehicles had the least influence at intersections.

Worldwide, traffic safety is a strong concern as traffic accidents are one of the leading causes of death. In this context, advanced driver assistance systems (ADAS) and autonomous vehicles are traffic management measures aimed at improving road safety and flow. Automatic detection and recognition of traffic signs are important for intelligent vehicles and ADAS systems. This work proposes a pipeline of digital image processing (DIP) techniques, machine learning (ML), and temporal coherence to perform the detection and recognition of Brazilian traffic signs in videos aiming an application for real-time systems to help in traffic safety and to reduce the number of fatal accidents. We are mainly interested in recognizing signs of speed limit group, no overtaking and obligatory passage, thus our detection considers the traffic sign with a circular shape and red border. For detection, the red color segmentation and the Hough transform are used to find circular regions that will be classified through the SVM algorithm in sign and not sign. For recognition of these signs, the support vector machines (SVM) are used. For speed limit signs the thresholding and contours are used to segment the digits for later classification. Our proposed method achieved an accuracy of 0.82 in detection, an increase of 18% in the number of recognized frames and 0.96 in the recognition stage using temporal coherence.

A critical aspect of designing road pavements is accurately estimating the expected traffic load. It's important to note that not all vehicles use the same wheel path when traveling on a road. Many factors, including the Lane Distribution Factor (LDF), can affect the lateral shift of the load applied to the pavement. LDF is a crucial factor in designing flexible pavements, as it helps transportation planners and highway engineers estimate lane usage relative to the total traffic volume. This information is essential for understanding how pavements respond to wheel loads and fatigue behavior. The IRC: 37 - 2018 guidelines provide a set of LDFs for estimating the design traffic in terms of Million Standard Axle Loads. However, to ensure the accuracy of these LDFs, a study was conducted on rural and urban highways with two and four lanes to identify the lateral distribution of the vehicles.