Mattias Brännström

A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

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 drivers 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.

A situation and threat assessment algorithm for a rear-end collision avoidance system

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.

Lane Change Maneuvers for Automated Vehicles

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.

If, When, and How to Perform Lane Change Maneuvers on Highways

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.

Longitudinal and lateral control for automated lane change maneuvers

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.

A Real-time Implementation of an Intersection Collision Avoidance System

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.

Longitudinal and Lateral Control for Automated Yielding Maneuvers

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.

Probabilistic threat assessment and driver modeling in collision avoidance systems

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.

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 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.

Safe autonomous lane changes in dense traffic

Lane change manoeuvres are complex driving manoeuvres to automate since the vehicle has to anticipate and adapt to intentions of several surrounding vehicles. Selecting a suitable gap to move/merge into the adjacent lane and performing the lane change can be challenging, especially in dense traffic. Existing gap selection methods tend to be either cautious or opportunistic, both of which directly affect the overall availability and safety of the autonomous feature. In this paper we present a method which enables the autonomous vehicles to increase the availability of lane change manoeuvres by reducing the required margins to ensure a safe manoeuvre. The required safety margins are first calculated by making use of the steering and braking capability of the vehicle. It is then shown that this method can be used to perform autonomous lane changes in dense traffic situations with small inter-vehicle gaps. The proposed solution is evaluated by using Model Predictive Control (MPC) to plan and execute the complete motion trajectory.