Julia Nilsson

Strategic decision making for automated driving on two-lane, one way roads using model predictive control

This paper presents an algorithm for strategic decision making regarding when lane change and overtake manoeuvres are desirable and feasible. By considering the task of driving on two-lane, one-way roads, as the selection of desired lane and velocity profile, the algorithm provides useful results in terms of velocity control as well as a decision variable corresponding to whether a lane change manoeuvre should be performed. The decision process is modelled through a mixed logical dynamical system which is solved through model predictive control using mixed integer program formulation. The performance of the proposed control system is explored through simulations of varying driving scenaria on a two-lane, one-way road, which shows the capability of the system to achieve appropriate longitudinal and lateral control strategies depending on the traffic situation.

Predictive manoeuvre generation for automated driving

This paper focuses on the problem of trajectory planning in an autonomous guidance application for one-way, two-lane roads. The problem is formulated in a receding horizon framework, as the minimization of the deviation from a desired velocity subject to a set of constraints introduced to avoid collision with surrounding vehicles, and to stay within the lane boundaries. As well known, this formulation can result in planning algorithms with prohibitive computational complexity, thus preventing real-time implementation. To avoid this limitation, the paper shows how the structured environment of one-way roads, can be exploited in order to formulate a low complexity receding horizon problem that can be efficiently solved in real-time. The proposed algorithm is demonstrated in simulations considering overtake manoeuvres.

Manoeuvre generation and control for automated highway driving

A hierarchical, two-level architecture for manoeuvre generation and vehicle control for automated highway driving is presented. The high-level planner computes a manoeuvre in terms of a (X, Y)-trajectory as well as a longitudinal velocity profile, utilizing a simplified point-mass model and linear collision avoidance constraints. The low-level controller utilizes a non-linear vehicle model in order to compute the vehicle control inputs required to execute the planned manoeuvre. Both the high-level planner and low-level controller are formulated based on the model predictive control methodology. Simulation results demonstrates the ability of the high-level planner to compute appropriate, traffic-dependent manoeuvres, that can be tracked by the low-level controller in real-time.

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.

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.

Rule-Based Highway Maneuver Intention Recognition

Future advanced driver assistance systems or fully automated driving systems require an increased ability to classify and interpret traffic situations in order to appropriately account for, and react to, the behavior of surroundings vehicles. When driving on a highway, humans are able to recognize the maneuver intentions of surrounding vehicles by observing lateral and longitudinal motion cues. The main idea presented in this paper is to adapt this ability to technical systems by formulating simple logic rules for intention recognition of highway maneuvers. As such, the presented algorithm is able to recognize the intention of left and right lane change maneuvers with an accuracy of approximately 89% while maintaining the false positive rate low at approximately 3%. Further, due to the algorithms low computational complexity, flexibility, and straight-forward design based on easily comprehensible logic rules, the proposed method fulfills the requirements of future advanced driver assistance systems or fully automated driving systems.