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Road safety has been studied for more than 80 years with the objective to prevent traffic accidents, injuries, and fatalities. Proper road design, standards, traffic rules, driver education and law enforcement are continuously improved to reduce the crash risk and enable mitigating actions. While significant advancements have been made, humans still frequently do mistakes, sometimes with severe consequences. In this paper we introduce the notion of Precautionary Safety and propose a methodology such that autonomous vehicles can adjust their trajectory planning to their capabilities, external conditions, and knowledge on human mistakes in order to satisfy overall requirements on accident-, injury- and fatality rates. More precisely, we describe how to make adjustments to existing driving policies so to satisfy real-world safety requirements, rather than only obeying to the law and traffic rules. As an illustrative example, the methods are applied to accident scenarios between vehicles and jaywalking pedestrians.

By considering a lane change maneuver as primarily a longitudinal motion planning problem, this paper presents a lane change maneuver algorithm with a pragmatic approach to determine an inter-vehicle traffic gap and time instance to perform the maneuver. The proposed approach selects an appropriate inter-vehicle traffic gap and time instance to perform the lane change maneuver by simply estimating whether there might exist a longitudinal trajectory that allows the automated vehicle to safely perform the maneuver. The lane change maneuver algorithm then proceeds to solve two loosely coupled convex quadratic programs to obtain the longitudinal trajectory to position the automated vehicle in the selected inter-vehicle traffic gap at the desired time instance and the corresponding lateral trajectory. Simulation results demonstrate the capability of the proposed approach to select an appropriate inter-vehicle traffic gap and time instance to initialize the lateral motion of a lane change maneuver in various traffic scenarios. The real-time ability of the lane change maneuver algorithm to generate safe and smooth trajectories is shown by experimental results of a Volvo V60 performing automated lane change maneuvers on a test track.

One of the major challenges of automated driving systems (ADS) is showing that they drive safely. Key to ensuring safety is eliciting a complete set of top-level safety requirements (safety goals). This is typically done with an activity called hazard analysis and risk assessment (HARA). In this paper we argue that the HARA of ISO 26262:2018 is not directly suitable for an ADS, both because the number of relevant operational situations may be vast, and because the ability of the ADS to make decisions in order to reduce risks will affect the analysis of exposure and hazards. Instead we propose a tailoring using a quantitative risk norm (QRN) with consequence classes, where each class has a limit for the frequency within which the consequences may occur. Incident types are then defined and assigned to the consequence classes; the requirements prescribing the limits of these incident types are used as safety goals to fulfil in the implementation. The main benefits of the QRN approach are the ability to show completeness of safety goals, and make sure that the safety strategy is not limited by safety goals which are not formulated in a way suitable for an ADS.

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.

Automated driving is predicted to enhance traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually realize fully automated driving, the intelligent vehicle system must have the ability to plan different maneuvers while adapting to the surrounding traffic environment. This paper presents an algorithm for longitudinal and lateral trajectory planning for automated driving maneuvers where the vehicle does not have right of way, i.e., yielding maneuvers. Such maneuvers include, e.g., lane change, roundabout entry, and intersection crossing. In the proposed approach, the traffic environment which the vehicle must traverse is incorporated as constraints on its longitudinal and lateral positions. The trajectory planning problem can thereby be formulated as two loosely coupled low-complexity model predictive control problems for longitudinal and lateral motion. Simulation results demonstrate the ability of the proposed trajectory planning algorithm to generate smooth collision-free maneuvers which are appropriate for various traffic situations.

Advanced driver assistance systems or highly automated driving systems for lane change maneuvers are expected to enhance highway traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually progress to highly automated highway driving, the task of automatically determine if, when, and how to perform a lane change maneuver, is essential. This paper thereby presents a low-complexity lane change maneuver algorithm which determines whether a lane change maneuver is desirable, and if so, selects an appropriate inter-vehicle traffic gap and time instance to perform the maneuver, and calculates the corresponding longitudinal and lateral control trajectory. The ability of the proposed lane change maneuver algorithm to make appropriate maneuver decisions and generate smooth and safe lane change trajectories in various traffic situations is demonstrated by simulation and experimental results.

This paper is concerned with the problem of decision-making in systems that assist drivers in avoiding collisions. An important aspect of these systems is not only assisting the driver when needed but also not disturbing the driver with unnecessary interventions. Aimed at improving both of these properties, a probabilistic framework is presented for jointly evaluating the driver acceptance of an intervention and the necessity thereof to automatically avoid a collision. The intervention acceptance is modeled as high if it estimated that the driver judges the situation as critical, based on the driver's observations and predictions of the traffic situation. One advantage with the proposed framework is that interventions can be initiated at an earlier stage when the estimated driver acceptance is high. Using a simplified driver model, the framework is applied to a few different types of collision scenarios. The results show that the framework has appealing properties, both with respect to increasing the system benefit and to decreasing the risk of unnecessary interventions.

This paper presents a real-time implementation of a collision avoidance (CA) system that uses autonomous braking and model predictive control to assist drivers in avoiding collisions with other road users. To the authors knowledge, this is the first CA system that targets general vehicle collisions that has been implemented in a car. The system is based on a recently published decision-making algorithm which is described in [1]. To validate the CA system in various collision scenarios without endangering the driver of the vehicle, a novel test platform has been developed. The test platform consist of a soft crashable obstacle which is movable in speeds up to 70 km/h and safe to collide with in any angle in relative speeds up to 100 km/h. In the current implementation, estimates of the motion of the obstacle are obtained through a reference sensor fusion system that is based on a combination of in-vehicle sensors and a differential global positioning system. Results from both intersection and rear-end collision situations are presented. The results show that the proposed CA system can be implemented in a real-time environment and that the predictive brake control algorithm accurately accounts for delays and ramp-up times in the brake system of the vehicle.

This paper presents a model-based algorithm that estimates how the driver of a vehicle can either steer, brake, or accelerate to avoid colliding with an arbitrary object. In this algorithm, the motion of the vehicle is described by a linear bicycle model, and the perimeter of the vehicle is represented by a rectangle. The estimated perimeter of the object is described by a polygon that is allowed to change size, shape, position, and orientation at sampled time instances. Potential evasive maneuvers are modeled, parameterized, and approximated such that an analytical expression can be derived to estimate the set of maneuvers that the driver can use to avoid a collision. This set of maneuvers is then assessed to determine if the driver needs immediate assistance to avoid or mitigate an accident. The proposed threat-assessment algorithm is evaluated using authentic data from both real traffic conditions and collision situations on a test track and by using simulations with a detailed vehicle model. The evaluations show that the algorithm outperforms conventional threat-assessment algorithms at rear-end collisions in terms of the timing of autonomous brake activation. This is crucial for increasing the performance of collision-avoidance systems and for decreasing the risk of unnecessary braking. Moreover, the algorithm is computationally efficient and can be used to assist the driver in avoiding or mitigating collisions with all types of road users in all kinds of traffic scenarios.

The objective of this study was to compare the individual board strength predictions from an X-ray log scanner by using either two or four X-ray directions. The benefit of applying traceability between log and board was also studied. In total, 119 Norway spruce [Picea abies (L.) Karst.] sawlogs were scanned by an X-ray log scanner at the log sorting station of a sawmill and sawn into two centre pieces per log. Individual board traceability was enabled by following the rotational position of the log in the scanner and at the succeeding sawing. All boards were graded by a commercial strength grading machine before destructive testing was done. The resulting data were used to derive variables for building multivariate partial least squares strength prediction models. In the modelling a hierarchical modelling approach was used, where annual ring width, dry density and elasticity were also modelled. For all concepts studied the models’ fit was similar. Only minor benefits could be found when using fou...

This paper considers the trajectory planning problem of a vehicle system for automated lane change maneuvers. By considering a lane change maneuver as primarily a longitudinal planning problem, the proposed trajectory planning algorithm determines whether there exists a longitudinal trajectory which allows the ego vehicle to safely position itself in a gap between surrounding vehicles in the target lane. If such a longitudinal trajectory exists, the algorithm plans the corresponding lateral trajectory. The lane change trajectory planning problem is thereby reduced to solving low-complexity model predictive control problems resulting in loosely coupled longitudinal and lateral motion trajectories. Simulation results demonstrate the ability of the proposed algorithm to generate smooth collision-free trajectories for lane change maneuvers.

This paper presents a probabilistic framework for decision-making in collision avoidance systems, targeting all types of collision scenarios with all types of single road users and objects. Decisions on when and how to assist the driver are made by taking a Bayesian approach to estimate how a collision can be avoided by an autonomous brake intervention, and the probability that the driver will consider the intervention as motivated. The driver model makes it possible to initiate earlier braking when it is estimated that the driver acceptance for interventions is high. The framework and the proposed driver model are evaluated in several scenarios, using authentic tracker data and a differential GPS. It is shown that the driver model can increase the benefit of collision avoidance systems — particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict, e.g. when a playing child enters the roadway.

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 motion model and polygons are used to describe its expected extension. The key idea is to parameterize the motion of the vehicle such that an analytical solution can be derived for estimating the set of manoeuvres that the driver can use to avoid the object at discrete times. The union of the solutions for all times is used to estimate how a collision can be avoided 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.

Rear-end collisions are common accident scenarios and a frequent cause of these accidents is driver distraction. This paper presents a situation assessment (SA) algorithm that estimates driver distraction by continuously assessing the steering actions of the driver. A collision avoidance (CA) system is proposed, which combines the SA with a threat assessment (TA) algorithm that estimates the effort needed to avoid a collision. It is shown that the SA algorithm proposed enables the CA system to initiate earlier brake interventions when the driver is assessed as being distracted, without significantly increasing the risk of false interventions in real traffic. The CA system has been evaluated in both collision situations on a test track and during 200 driving hours in real traffic conditions.

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.