Decision-Making in Automotive Collision Avoidance Systems

This thesis is concerned with decision-making in systems that can detect hazardous traffic situations and assist drivers in avoiding collisions by using automatic braking or steering. The aim with these systems is to reduce the number of accidents and their consequences without disturbing the driver with unnecessary interventions during normal traffic conditions.The main contribution of this thesis consists of algorithms for evaluating if the driver needs assistance to avoid colliding with a single road user in any traffic situation. The proposed algorithms, which are shown to work well in a real-time environment, are evaluated using data from both real traffic conditions, simulations and collision situations on a test track. Moreover, a probabilistic decision-making framework is presented for jointly evaluating the driver acceptance of an intervention and the necessity thereof to automatically avoid an accident. The framework enables earlier interventions in critical traffic situations, thereby increasing the benefit of the system. Additionally, a method is proposed for estimating driver distraction by observing the driver\u27s steering behavior prior to near-crash situations. It is shown that earlier interventions can be triggered when the driver is assessed as being distracted without significantly increasing the risk of unnecessary interventions. Decision-making on when to assist the driver by steering and when to assist by braking is discussed and an algorithm for finding suitable evasive steering maneuvers to pass between multiple moving objects is presented

On Threat Assessment and Decision-Making for Avoiding Automotive Vehicle Collisions

Road traffic accidents are one of the world’s largest public health problems. In the EU alone, traffic accidents cause approximately 1.8 million injuries and 43.000 fatalities each year. This thesis is concerned with the development of in-vehicle systems that can detect hazardous traffic situations and assist drivers in avoiding or mitigating accidents.An overview is given of different types of accidents and measures that are taken to reduce the number of accidents and their consequences. From this overview, certain types of accidents have been selected to be addressed in this research project. The contribution of this thesis is a number of algorithms that can assess traffic situations and make decisions to actively assist the driver in avoiding or mitigating these accident types.The approach that is used for making decisions on when and how to assist the driver, is to first estimate how a collision can be avoided by the driver. Secondly, the brakes are applied autonomously if hard braking is the only option to avoid or mitigate an accident. The algorithms proposed in this thesis are capable of estimating how collisions can be avoided in any type of collision scenario, such as rear-end collisions andintersection collisions. These algorithms can be used to avoid or mitigate collisions with all types of road users, such as pedestrians, cyclists and other vehicles.The algorithms have been evaluated using data from both real traffic conditions and collision situations on a test track. The results show that the algorithms can improve the performance of conventional rear-end collision avoidance systems, without significantly increasing the risk of unnecessary braking

Method and system for handling conditions of a road on which a vehicle travels

Autonomous vehicles need to drive safely, also considering challenging use cases related to tire-to-road friction. This patent application describes how to detect treacherous road friction conditions and safe strategies for driving under said conditions

Threat assessment for avoiding collisions with turning vehicles

This paper presents a method for estimating how the driver of a vehicle can use steering, braking or acceleration to avoid a collision with a moving object. In the method, the motion of the object can be described with an arbitrary deterministic motion model and polygons can be used to describe its extension. The key idea is to derive the analytical solution on how to avoid the object at discrete times. The union of the solutions for all times is used to estimate how to avoid a collision during the complete prediction horizon. Additionally, a decision-making algorithm is proposed that decides when to initiate autonomous braking to avoid or mitigate a potential collision. A collision avoidance by braking system, based on the proposed method and algorithm, has been evaluated on simulated traffic scenarios at intersections. It is shown that a vehicle equipped with such a system can potentially reduce the impact velocity with up to 40~km/h in left turn across path situations

Driving strategies and detection for treacherous road condition

Method and system for handling conditions of a road on which a vehicle travel

Decision Making on when to Brake and when to Steer to Avoid a Collision

By either autonomously steering or braking, accidents can be avoided or mitigated by a number of active safety functions which are available on the market today. However, these functions are often tailored for specific accident types and for each type either braking or steering may be possible. This contribution considers an algorithm for threat assessment which can be used in general traffic situations not only to decide if an intervention is necessary to avoid an accident, but also to select which type of intervention, steering or braking. The algorithm is evaluated on four accident types; rear-end accidents showing how the appropriate intervention depends on the lateral offset between host vehicle and target vehicle, single-target straight crossing path collisions where the decision depends on the vehicles’ speed, collision scenarios with oncoming vehicles and finally situations where multiple obstacles need to be considered

Decision Making on when to Brake and when to Steer to Avoid a Collision

By either autonomously steering or braking, accidents can be avoided or mitigated by a number of active safety functions which are available on the market today. However, these functions are often tailored for specific accident types and for each type either braking or steering may be possible. This contribution considers an algorithm for threat assessment which can be used in general traffic situations not only to decide if an intervention is necessary to avoid an accident, but also to select which type of intervention, steering or braking. The algorithm is evaluated on four accident types; rear-end accidents showing how the appropriate intervention depends on the lateral offset between host vehicle and target vehicle, single-target straight crossing path collisions where the decision depends on the vehicles’ speed, collision scenarios with oncoming vehicles and finally situations where multiple obstacles need to be considered

Threat assessment for avoiding collisions with turning vehicles

This paper presents a method for estimating how the driver of a vehicle can use steering, braking or acceleration to avoid a collision with a moving object. In the method, the motion of the object can be described with an arbitrary deterministic motion model and polygons can be used to describe its extension. The key idea is to derive the analytical solution on how to avoid the object at discrete times. The union of the solutions for all times is used to estimate how to avoid a collision during the complete prediction horizon. Additionally, a decision-making algorithm is proposed that decides when to initiate autonomous braking to avoid or mitigate a potential collision. A collision avoidance by braking system, based on the proposed method and algorithm, has been evaluated on simulated traffic scenarios at intersections. It is shown that a vehicle equipped with such a system can potentially reduce the impact velocity with up to 40~km/h in left turn across path situations

Learning faster to perform autonomous lane changes by constructing maneuvers from shielded semantic actions

This paper introduces a new method to solve tactical decision making problems for highway lane changes. In the system design, reference sets for low level controllers are employed to formulate semantic meaningful actions used by reinforcement learning algorithm. Safety is ensured by preemptively shielding the Markov decision process (MDP) from unsafe actions. This frees the agent to focus on learning how to interact efficiently with the surrounding traffic. By introducing human demonstration with supervised loss as better exploration strategy, the learning process and initial performance are boosted further.\ua0\ua9 2019 IEEE