Motorway Flow Quality Impacts of Advanced Driver Assistance Systems

Compared to the motorway network, rural and urban roads are very unsafe. Advanced Driver Assistance Systems can be used to increase traffic safety, thereby improving the conditions for effective deployment of the underlying network for integrated traffic management. In this study, we have identified ADA systems that are expected to have a high impact on traffic safety by coupling characteristics of accidents to the functionality of different ADA systems. Based on the results of this study, Intelligent Speed Adaptation (ISA) and Intersection Crash Avoidance systems appeared most likely to substantially improve the safety on non-motorway roads. ISA was selected for further analysis due to the likelihood of timely deployment of the system. Microscopic simulation model was used to quantify the impacts on traffic safety for an urban highway with controlled intersections near the Dutch city of Delft. Depending on the penetration level, the results of the simulation study showed that the decrease in the total number of accidents ranges from 4% to 19%.

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

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.

A collision model for safety evaluation of autonomous intelligent cruise control

In general intelligent transport systems (ITS) provide increased automation and optimization to the traffic system and into the vehicles. Many systems to support driving are already available in commercial products, like intelligent speed adaptation and cruise control, lane keeping support, lane change assistant, automated emergency braking etc. Electric vehicle technology is generally considered as a solution to reduce traffic emissions and environmental load. While electricity as an energy form of a vehicle is only a way to store and transfer primary energy, the full environmental benefits of electric vehicles (EVs) are gained only if energy consumption of the traffic will be reduced along transition to electric vehicle technology. The combination of EV technology and lTS offers a remarkable potential to achieve the goals set to both EVs and lTS. Electric propulsion technology offers outstanding platform for lTS to control the functions of a vehicle. On the other hand electric propulsion technology makes possible intelligent functions and vehicle concepts that were not possible with internal combustion engine technology. This paper describes why and how the combination of EVs and lTS, intelligent electric mobility, is organized and what are the benefits of this combination.

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.

Summary form only given. In view of the projected future increase in road traffic, the automobile industry is involved with on-going research activities ranging from traffic safety, environmental considerations of the motor industry, and navigational traffic information systems to name a few. Work that is currently being undertaken between the Control Theory and Applications Center and Jaguar Cars Limited in the area of autonomous intelligent cruise control (AICC) is discussed, with particular emphasis directed towards issues related to automatic control.

The levels of driver comfort and acceptance for an autonomous intelligent cruise control (AICC) system driven in an actual highway environment are investigated. Objective measures of driving performance and behaviour are compared with participants' subjective assessments when operating under manual control, conventional cruise, and AICC. Included in the comparison are measures of driver velocity and braking behaviour. Participants drive slightly higher mean velocities under the manual condition as compared with AICC. Participants applied the brakes less frequently when driving manually. Participants rated the AICC system favorably for comfort, ease of use, and convenience. However, participants did express limited concerns associated with the use of AICC. (A) For the covering abstract of the conference see IRRD 875189.

• 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

Simulation of vehicle traffic on an automated highway system

An autonomous intelligent cruise control system was designed and simulated based on measured relative distance, speed, and acceleration. These constitute the fuzzy inputs. The results have shown that the system can be robust to noisy distance measurements and modular in structure to ease its implementation. A simple way of estimating the road condition was developed, implemented and tested in simulation. The results show that based on the measured relationship between the deceleration and brake pressure, the brake pressure output gain can be adjusted to prevent lock up of the tires and the consequent loss of stability. Two controller implementations were tested. The first one is based on heuristic knowledge of the system using Mamdani inference system. The second model was based on offline adaptive neuro-fuzzy controller model.

Operationalizing adaptive policymaking

Smart cities integrate data collection and communication technologies to operate more efficiently in order to provide better services to citizens, improved road traffic situation, better economic development, etc. However, safety still a main concern of road traffic as road crashes pose serious threats to road users causing death and injury all over the world. Road traffic safety is the process of implementing measures to reduce the number of crashes, death and severe injuries. In this paper, using Machine Learning (ML) algorithms, Off-line, we train and test the Intelligent and Dynamic Adaptation Model (IDAM) on the road traffic data collected for 14 years of car crashes in United Kingdom (UK). IDAM then on-line determines the optimal speed limit for each road segment based on many predictors such as weather, road type, light condition, and many others, stated later. We train and test IDAM using three different machine learning algorithms: Artificial Neural Network (ANN), Decision Tree (DT), and Linear Regression (LR) with Stochastic Gradient Descent (SGD). Using the testing dataset, IDAM achieves prediction accuracy of around 96%. Implemented in an operation algorithm, IDAM can continuously adapt the road speed limit to an optimal value as the predictors change on-line.

<div class="htmlview paragraph">Radar based collision warning systems (CWS) for heavy trucks have been in production and on the road in the United States for approximately two years. A short description of these systems is presented with actual driver performance data and accident reduction data as experienced by various U.S. national fleets using these systems. The next application of radar sensors is for Intelligent Cruise Control (ICC) or also known as Adaptive Cruise Control (ACC). This paper describes the operation of adaptive cruise control and presents the associated benefits to the driver</div> <div class="htmlview paragraph">The radar technology has been enhanced and the application has been extended to include radar controlled adaptive cruise control for heavy trucks This has been made possible by 1) technology improvements in radar systems, plus 2) the addition of the standard SAE J1939 data bus for engine controllers on most U.S diesel engines. The SAE J1939 data bus allows electronic control of engine speed by the radar system plus in many cases, control of engine retarder braking to assist in vehicle slowing while in adaptive cruise control operation.</div> <div class="htmlview paragraph">This paper described the radar operation, radar enhancements and the benefits of adaptive cruise control as experienced by drivers. The most significant radar enhancement, called monopulse radar is presented Comparisons are made between standard cruise control operation on heavy trucks and adaptive cruise control</div>