Automotive Threat Assessment Design for Combined Braking and Steering Maneuvers

The active safety systems available on the passenger cars market today, automatically deploy automated safety interventions in situations where the driver is in need of assistance. In this paper, we consider the process of determining whether such interventions are needed. In particular, we design a threat assessment method which evaluates the risk that the vehicle will either leave the road or its maneuverability will be significantly reduced within a finite time horizon. The proposed threat assessment method accounts for combined braking and steering maneuvers, which results in a nonlinear dynamical vehicle behavior. We formulate the threat assessment problem as a nonconvex constraint satisfaction problem and implement an algorithm that solves it through interval-based consistency techniques. Experimental validation of the proposed approach indicates that constraint violation can be predicted, while avoiding the detection of false threats

A PREDICTIVE APPROACH TO ROADWAY DEPARTURE PREVENTION

In this paper, we investigate predictive control approaches to the problem of roadway departure prevention via steering and braking. We assume a sensing infrastructure detecting road geometry and consider a two layers architecture consisting of a threat assessment and an intervention layer. In particular, the upper threat assessment layer detects the risk of roadway departure or vehi- cle instability within a future time horizon. If a risk of roadway departure or vehicle instability is detected, the lower intervention layer is enabled. The lat- ter is designed based on Model Predictive Control (MPC) approaches, where steering and braking interventions are the result of an optimization problem. This is formulated on the basis of vehicle state measurements and coming road information (e.g., road geometry, surface adhesion) and repeatedly solved over a moving future time horizon. Simulation and experimental results are presented, showing that the proposed approach effectively exploits road preview capabilities in order to issue earlier and less intrusive interventions, compared to standard Electronic Stability Con- trol (ESC) systems

Approaches to Enhance Driver Situational Assessment Aids

Collision warning systems encounter a fundamental trade-off between providing the driver more time in which to respond and alerting the driver unnecessarily. The probability that a driver successfully avoids a hazard increases as the driver is provided more time and distance in which to identify the hazard and execute the most effective response. However, alerting the driver at earlier, more conservative thresholds increases the probability that the alerts are unnecessary, either because sensor error has falsely identified a hazard or because the environment has changed such that a hazard is no longer a threat. Frequent unnecessary alerts degrade alert effectiveness by reducing trust in the system. The human-factors issues pertaining to a forward collision warning system (FCWS) were analyzed using an Integrated Human-Centered Systems approach, from which two design features were proposed: multi-stage alerting, which alerts the driver at a conservative early threshold, in addition to a more serious late threshold; and directional alerting, which provides the driver information regarding the location of the hazard that prompted the alert activation. Alerting the driver earlier increases the probability of a successful response by conditioning the driver to respond more effectively if and when evasive action is necessary. Directional alerting decreases the amount of time required to identify the hazard, while promoting trust in the system by informing the driver of the cause of the alert activation. The proposed design features were incorporated into three FCWS configurations, and an experiment was conducted in which drivers were equipped with the systems and placed in situations in which a collision would occur if they did not respond. Drivers who were equipped with multi-stage and directional alerting were more effective at avoiding hazardous situations than drivers who were not provided early alerting. Drivers with early alerting tended to respond earlier and more consistently, which promoted more successful responses. Subjective feedback indicates that drivers experienced high levels of acceptance, confidence, and trust in multi-stage and directional alerting.This work was funded by a grant provided through the Ford-MIT Alliance

Computational driver behavior models for vehicle safety applications

The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

Trends in vehicle motion control for automated driving on public roads

In this paper, we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk manoeuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally, the important topic of verification of driving automation systems is addressed

Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling

Traffic safety is a problem worldwide. In-vehicle conflict and crash avoidance systems have been under development and assessment for some time, as integral parts of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Among the methods used to assess conflict and crash avoidance systems developed by the automotive industry, virtual safety assessment methods have been shown to have great potential and efficiency. In fact, scenario generation-based virtual safety assessments play—and are likely to continue to play—a very important role in the assessments of vehicles of all levels of automation. The ultimate aim of this thesis is to improve the safety performance of conflict and crash avoidance systems. This aim is addressed through the use of computational driver models in two different ways. First, by using comfort-zone boundaries in system design, and second, by using a behavior-based crash-causation model together with a novel optimized scenario generation method for virtual safety assessment.The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run

Threat assessment algorithm for Active Blind Spot Assist system using short range radar sensor

Road safety has become more concern due to the number of accidents that keeps increasing every year. The safety systems include from simple installation such as seat belt, airbag, and rear camera to more complicated and intelligent systems such as braking assist, lane change assist, steering control and blind spot monitoring. This paper proposes another intelligent safety system to be implemented in passenger vehicle by monitoring the blind-spot region by using automotive short range radar as sensor to assess its surrounding. This system is called Active Blind-Spot Assist (ABSA) system and this system will collaborate with a Steering Intervention system for autonomous steering maneuvers. The objective of ABSA system is to deploy safety interventions by giving warning to the driver whenever other vehicle is detected within the blind-spot region. Furthermore, this active system also triggers autonomous steering control when the potential of collision with the detected vehicle increases greatly. Consequently, a threat assessment algorithm is developed to evaluate the right moment to give safety interventions to the driver and the conditions for autonomous steering maneuvers. The process of developing the threat assessment algorithm explained in this paper

Center of Gravity Estimation and Rollover Prevention Using Multiple Models & Controllers

In this paper, we present a methodology based on multiple models and switching for realtime estimation of center of gravity (CG) position and rollover prevention in automotive vehicles. Based on a linear vehicle model in which the unknown parameters appear nonlinearly, we propose a novel sequential identification algorithm to determine the vehicle parameters rapidly in real time. The CG height estimate is further coupled with a switching controller to prevent untripped rollover in automotive vehicles. The efficacy of the proposed switched multi model/controller estimation and control scheme is demonstrated via numerical simulations

Passenger kinematics in evasive maneuvers

In situations that might lead to a vehicle crash, drivers often perform an evasive maneuver, such as braking or steering, in an attempt to avoid a crash. If a crash was not avoided, the maneuver could influence the injury outcome by altering the occupant’s position. Occupants use their muscles in response to a maneuver, and because the typical accelerations are low during maneuvers, the muscle activity can influence the kinematics. Thus, it is important to include the response to these potential maneuvers before the crash when predicting occupant injuries in a crash. The response to maneuvers could be evaluated by adding active musculature to existing evaluation tools, such as human body models. Furthermore, in volunteer studies, the head and torso displacements during maneuvers vary between occupants, but the cause for this variability remains to be identified. Two aims were defined for this thesis, addressed in two parts. The first aim was to advance the active neck and lumbar muscle controllers in the SAFER HBM to predict average response to maneuvers. The second aim was to further understand why such variability is seen in occupant response to evasive maneuvers.Three muscle controller concepts were evaluated in this thesis, two of which were aimed at emulating the reflexes responding to input from the vestibular system that control the head position in space, and one controller that emulated reflexes that respond to lengthening of muscles. For the first aim, the active muscle controllers in the SAFER HBM were updated to allow for simulations with large vehicle yaw rotations, and the predictive capabilities were evaluated in braking, steering, and combinations. In a subsequent study, the updated controllers were tuned to volunteer kinematics in braking and steering, and the model performance was evaluated in the same conditions. It was concluded that the SAFER HBM, with the updated and tuned controllers, could predict passenger head kinematics in braking and steering with good to excellent results.The occupant variability was addressed by statistical analysis of volunteer kinematics in six different vehicle maneuvers. In two subsequent studies, the Active Human Body Model developed within the first aim was used to analyze the model sensitivity to Human Body Model and boundary condition characteristics in braking. From the analysis of volunteer kinematics, it was concluded that the belt system was the most influential predictor for head and torso displacements across all maneuvers, while other characteristics such as sex, stature, age, and body mass index were less influential. In the subsequent studies, the seat forward/rearward position and spinal curvature were found to be most influential in braking

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program