A systematic review on the autonomous emergency steering assessments and tests methodology in ASEAN

Safety should be the top priority for any automaker - because traffic accidents roughly killed 1.4 million people worldwide, ranking tenth on the World Health Organization's list of leading causes of death. Two decades ago, the focus was on passive safety, where it helps vehicle occupants to survive the crash. However, the frontier in safety innovation has moved beyond airbags and side-impact protection. Today, the frontier is active safety for preventing collisions before they occur. In Euro NCAP 2025 Roadmap, this active safety frontier falls under the primary safety and has become one of the overall safety rating initiatives toward safer cars. The primary safety features four technologies to be assessed, including driver monitoring (2020), automatic emergency steering (2020, 2022), autonomous emergency braking (2020, 2022), and V2x (2024). However, this initiative is partially encapsulated in the ASEAN NCAP Roadmap 2021-2025 under – 'Safety Assist' technological feature. For instance, in the new roadmap, ASEAN NCAP only focuses on Auto Emergency Braking (AEB) technology. This AEB is a feature to alert drivers to an imminent crash and help them use the car's maximum capacity. Therefore, as benchmarked to the EURO NCAP, this paper comprehensively reviews the AES demand, assessments, control, and testing methodology and can be further developed to consolidate for the ASEAN NCAP safety rating schemes

Understanding and modelling car drivers overtaking cyclists: Toward the inclusion of driver models in virtual safety assessment of advanced driving assistance systems

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. To help car drivers avoid or mitigate crashes while overtaking a cyclist, advanced driver assistance systems (ADAS) have been developed. To evaluate and further improve these ADAS to support drivers as they overtake cyclists, we need to understand and model driver behaviours.This thesis has two objectives: 1) to extract and analyse cyclist-overtaking manoeuvres from naturalistic driving data and 2) compare driver behaviour models for overtaking manoeuvres that can be used in counterfactual simulations for evaluating ADAS safety benefits.The drivers’ comfort zone boundaries (CZBs) when overtaking a cyclist were identified and analysed using naturalistic driving data. Three driver models that predict when a car driver starts steering away in order to overtake a cyclist were implemented: a threshold model, an evidence accumulation model, and a model inspired by a proportional-integral-derivative controller. These models were tested and verified using two different datasets, one from a test-track experiment and one from naturalistic driving data. Model parameters were obtained using computationally efficient linear programming.The results show that, when an oncoming vehicle was present, the drivers were significantly closer to the cyclist before steering away. This finding indicates that the presence of an oncoming vehicle is a crucial factor for the safety of the cyclist and needs to be taken into account for the development of ADAS that maintain safe distance to the cyclist. Furthermore, the quantification of the CZBs has implications for the development of ADAS which can estimate the time-to-collision to an oncoming vehicle or a cyclist to be overtaken, providing timely and acceptable warnings—or interventions—when drivers exceed their usual CZBs. A comparison of the models shows that all three are highly variable in detecting steering away time for different drivers. Furthermore, differences were discovered in detected steering away time between models fitted to test-track experiments and naturalistic driving data. Future work may focus on using larger, more diverse datasets and investigating more advanced models before including them in counterfactual simulations

Driver interaction with vulnerable road users: Modelling driver behaviour in crossing scenarios

Every year, more than 5000 pedestrians and 2000 cyclists die on European roads. These vulnerable road users (VRUs) are especially at risk when interacting with cars. Intelligent safety systems (ISSs), designed to mitigate or avoid crashes between cars and VRUs, first entered the market a few years ago, and still need to be improved to be effective. Understanding how drivers interact with VRUs is crucial to improving the development and the evaluation of ISSs. Today, however, there is a lack of knowledge about driver behaviour in interactions with VRUs. To address this deficiency and contribute to realising the full potential of ISSs, this thesis has multiple objectives: 1) to investigate and describe the driver response process when a VRU crosses the driver path, 2) to devise models that can predict the driver response process, 3) to inform Euro NCAP with new knowledge about driver interactions with crossing VRUs that may guide the development of their test scenarios, and 4) to develop a framework for ISS evaluation through counterfactual simulation and analyse the impact of the chosen driver model on the simulation outcome. The thesis results show that the moment when a VRU becomes visible to the driver has the largest influence on the driver’s braking response process in driver-VRU interactions. Data gathered in driving simulators and on a test track were used to devise different predictive models: one model for the pedestrian crossing scenario, and three for the cyclist crossing scenario. The model for the pedestrian crossing scenario can estimate the moments at which key components of the driver response process (e.g. gas pedal fully released and brake onset) happen. For the cyclist crossing scenario, the first model predicts the brake onset time and the second predicts the experienced discomfort score given the cyclist appearance time. The third predicts the continuous deflection signal of the brake pedal based on the interaction of two visually-derived cues (looming and projected post-encroachment time). These models could be used to improve the design and evaluation of ISSs. From the models, appropriate warning or intervention times that are not a nuisance to the drivers could be adopted by the ISSs, therefore maximizing driver acceptance. Additionally, the models could be used in counterfactual simulations to evaluate ISS safety benefits. In fact, it was shown that driver models are a critical part of these simulations, further demonstrating the need for the development of more realistic driver models. The knowledge provided by this thesis may also guide Euro NCAP towards an improved ISS test protocol by providing information about scenarios that have not yet been evaluated

Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing

open access articleThe on-board sensors of connected autonomous vehicles (CAVs) are limited by their range and inability to see around corners or blind spots, otherwise known as non-line of sight scenarios (NLOS). These scenarios have the potential to be fatal (critical scenarios) as the sensors may detect an obstacle much later than the amount of time needed for the car to react. In such cases, mechanisms such as vehicular communication are required to extend the visibility range of the CAV. Despite there being a substantial body of work on the development of navigational and communication algorithms for such scenarios, there is no standard method for generating and selecting critical NLOS scenarios for testing these algorithms in a scenario-based simulation environment. This paper puts forward a novel method utilising a genetic algorithm for the selection of critical NLOS scenarios from the set of all possible NLOS scenarios in a particular road environment. The need to select critical scenarios is pertinent as the number of all possible driving scenarios generated is large and testing them against each other is time consuming, unnecessary and expensive. The selected critical scenarios are then validated for criticality by using a series of MATLAB based simulations

Non-Line of Sight Test Scenario Generation for Connected Autonomous Vehicle

Connected autonomous vehicles (CAV) level 4-5 use sensors to perceive their environment. These sensors are able to detect only up to a certain range and this range can be further constrained by the presence of obstacles in its path or as a result of the geometry of the road, for example, at a junction. This is termed as a non-line of sight (NLOS) scenario where the ego vehicle (system under test) is unable to detect an oncoming dynamic object due to obstacles or the geometry of the road. A large body of work now exist which proposes methods for extending the perception horizon of CAV’s using vehicular communication and incorporating this into CAV algorithms ranging from obstacle detection to path planning and beyond. Such proposed new algorithms and entire systems needs testing and validating, which can be conducted through primarily two ways, on road testing and simulation. On-road testing can be extremely expensive and time-consuming and may not cover all possible test scenarios. Testing through simulation is inexpensive and has a better scenario space coverage. However, there is currently a dearth in simulated testing techniques that provides the environment to test technologies and algorithms developed for NLOS scenarios. This thesis puts forward a novel end-to-end framework for testing the abilities of a CAV through simulated generation of NLOS scenarios. This has been achieved through following the development process of Functional, Logical and Concrete scenarios along the V-model-based development process in ISO 26262. The process begins with the representation of the NLOS environment (including the digital environment) knowledge as a scalable ontology where Functional and Logical scenarios stand for different abstraction levels. The proposed new ontology comprises of six layers: ‘Environment’, ‘Road User’, ‘Object Type’, ‘Communication Network’, ‘Scene’ and ‘Scenario’. The ontology is modelled and validated in protégé software and exported to OWL API where the logical scenarios are generated and validated. An innumerable number of “concrete” scenarios are generated as a result of the possible combinations of the values from the domains of each concept’s attributes. This research puts forward a novel genetic- algorithm (GA) approach to search through the scenario space and filter out safety critical test scenarios. A critical NLOS scenario is one where a collision is highly likely because the ego vehicle was unable to detect an obstacle in time due to obstructions present in the line-of-sight of the sensors or created due to the road geometry. The metric proposed to identify critical scenarios which also acts as the GA’s fitness function uses the time-to-collision (TTC) and total stopping time (TST) metric. These generated critical scenarios and proposed fitness function have been validated through MATLAB simulation. Furthermore, this research incorporates the relevant knowledge of vehicle-to-vehicle (V2V) communication technologies in the proposed ontology and uses the communication layer instances in the MATLAB simulation to support the testing of the increasing number of approaches that uses communications for alerting oncoming vehicles about imminent danger, or in other word, mitigating an otherwise critical scenario

Computational Verification Methods for Automotive Safety Systems

This thesis considers computational methods for analysis and verification of the class of automotive safety systems which support the driver by monitoring the vehicle and its surroundings, identifying hazardous situations and actively intervening to prevent or mitigate consequences of accidents. Verification of these systems poses a major challenge, since system decisions are based on remote sensing of the surrounding environment and incorrect decisions are only rarely accepted by the driver. Thus, the system must make correct decisions, in a wide variety of traffic scenarios. There are two main contributions of this thesis. First, theoretical analysis and verification methods are presented which investigate in what scenarios, and for what sensor errors, the absence of incorrect system decisions may be guaranteed. Furthermore, methods are proposed for analyzing the frequency of incorrect decisions, including the sensitivity to sensor errors, using experimental data. The second major contribution is a novel computational framework for determining the errors of mobile computer vision systems, which is one of the most widely used sensor technologies in automotive safety systems. Augmented photo-realistic images, generated by rendering virtual objects onto a real image background, are used as input to the computer vision system to be tested. Since the objects are virtual, ground truth is readily available and varying the image content by adding different virtual objects is straightforward, making the proposed framework flexible and efficient. The framework is used for both performance evaluation and for training object classifiers

Computational Verification Methods for Automotive Safety Systems

This thesis considers computational methods for analysis and verification of the class of automotive safety systems which support the driver by monitoring the vehicle and its surroundings, identifying hazardous situations and actively intervening to prevent or mitigate consequences of accidents. Verification of these systems poses a major challenge, since system decisions are based on remote sensing of the surrounding environment and incorrect decisions are only rarely accepted by the driver. Thus, the system must make correct decisions, in a wide variety of traffic scenarios. There are two main contributions of this thesis. First, theoretical analysis and verification methods are presented which investigate in what scenarios, and for what sensor errors, the absence of incorrect system decisions may be guaranteed. Furthermore, methods are proposed for analyzing the frequency of incorrect decisions, including the sensitivity to sensor errors, using experimental data. The second major contribution is a novel computational framework for determining the errors of mobile computer vision systems, which is one of the most widely used sensor technologies in automotive safety systems. Augmented photo-realistic images, generated by rendering virtual objects onto a real image background, are used as input to the computer vision system to be tested. Since the objects are virtual, ground truth is readily available and varying the image content by adding different virtual objects is straightforward, making the proposed framework flexible and efficient. The framework is used for both performance evaluation and for training object classifiers