Many traffic incidents are due to the driver’s lack of attention, resulting in dangerous lane departures, either sliding off theroad or into the oppose lane. These kinds of incidents often have serious outcomes, which has led to much effort being concentrated on preventing or lessening the damages when the incident is already a fact, for example by installing safety belts and air bags. These measures may be considered to be acts of so-called passive safety. Active safety on the other hand, means that the safety systems intervene before the incidents have occurred. Lane Keeping Aid (LKA), which has been developed and implemented in this master thesis project, is a system designed to support the driver in the lateral axis in situations when unwanted lane departure is an evident risk. To be able to determine when the system should intervene and support the driver, information regarding how the driver handles the vehicle, along with the vehicle’s position and direction in the lane, is essential. The car’s position may be obtained by installing a camera in the vehicle. The information needed regarding other things, e.g. the car’s position in relation to the lane, is obtained by using a Kalmanfilter, which is based on a physically developed model, and which estimates the mentioned distances. Based on measurements and estimated values, it is possible for the LKA system to calculate an assistance torque, aimed at decreasing the lateral deviation from the centre of the lane. An electric power steering, instead of a conventional hydraulic steering servo is then used to produce the torque. The LKA system has been developed in a simulation environment using Simulink before being implemented, in order to monitor the function of the system before beginning actual testdrives. Furthermore, real measurement data given at driving with the test vehicle has been used to adjust and test the function. The results from the project’s first phase, in the simulation environment, show that the estimated values from the Kalmanfilter correlates well with real test data. Simulations with real measurement data show that the system functions as intended. Finally, it may also be mentioned, that the system has yet not been fully tested in a vehicle equipped with an electric power steering, which ought to be included in future development of the system.

<p>Many traffic incidents are due to the driver’s lack of attention, resulting in dangerous lane departures, either sliding off theroad or into the oppose lane. These kinds of incidents often have serious outcomes, which has led to much effort being concentrated on preventing or lessening the damages when the incident is already a fact, for example by installing safety belts and air bags. These measures may be considered to be acts of so-called passive safety. </p><p>Active safety on the other hand, means that the safety systems intervene before the incidents have occurred. Lane Keeping Aid (LKA), which has been developed and implemented in this master thesis project, is a system designed to support the driver in the lateral axis in situations when unwanted lane departure is an evident risk. </p><p>To be able to determine when the system should intervene and support the driver, information regarding how the driver handles the vehicle, along with the vehicle’s position and direction in the lane, is essential. The car’s position may be obtained by installing a camera in the vehicle. The information needed regarding other things, e.g. the car’s position in relation to the lane, is obtained by using a Kalmanfilter, which is based on a physically developed model, and which estimates the mentioned distances. Based on measurements and estimated values, it is possible for the LKA system to calculate an assistance torque, aimed at decreasing the lateral deviation from the centre of the lane. An electric power steering, instead of a conventional hydraulic steering servo is then used to produce the torque. </p><p>The LKA system has been developed in a simulation environment using Simulink before being implemented, in order to monitor the function of the system before beginning actual testdrives. Furthermore, real measurement data given at driving with the test vehicle has been used to adjust and test the function. </p><p>The results from the project’s first phase, in the simulation environment, show that the estimated values from the Kalmanfilter correlates well with real test data. Simulations with real measurement data show that the system functions as intended. </p><p>Finally, it may also be mentioned, that the system has yet not been fully tested in a vehicle equipped with an electric power steering, which ought to be included in future development of the system.</p>

We consider first the single-track bicycle model and state relations between the tires’ lateral forces and the turning radius. From the tire model, a relation between the lateral forces and slip angles is obtained. The extra rolling resistance forces from cornering are by linear approximation obtained as a function of the slip angles. The bicycle model is validated against the Magic-formula tire model from Adams. The bicycle model is then applied on an optimization problem, where the optimal velocity for a track for some given test cases is determined such that the energy loss is as small as possible. Results are presented for how much fuel it is possible to save by driving with optimal velocity compared to fix average velocity. The optimization problem is applied to a specific laden truck. / Vi betraktar först den enspåriga cykelmodellen och ställer upp samband mellan däckens sidokrafter och kurvradien. Genom däcksmodellen fås ett samband för hur sidokrafterna beror av slipvinklarna. De extra rullmotståndskrafterna för kurvor fås via linjär approximation som funktion av slipvinklarna. Cykelmodellen valideras mot en däcksmodell från Adams. Cykelmodellen tillämpas sedan på ett optimeringsproblem där den optimala hastigheten längs en bana för några givna testfall bestäms så att energiförlusten blir så liten som möjligt. Resultat presenteras för hur mycket bränsle det är möjligt att spara genom att köra med optimal hastighet jämfört med fix medelhastighet. Optimeringsproblemet tillämpas på en specifik lastad lastbil.