Autonomous Overtaking Assistant for Country Road Scenarios

Sulejmani, Fisnik; Reiterer, Florian; Assadi, Amin; Re, Luigi del

doi:10.23919/acc45564.2020.9147862

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…In various works, Bayesian networks have been used to predict the behavior of surrounding vehicles in different traffic scenarios to improve fuel economy, driving comfort and safety [23,24,25] and also for overtaking [26], but the focus lays mostly on the vehicles, and topological factors of the environment are hardly considered.…”

Section: Structure and Aim Of This Workmentioning

confidence: 99%

“…For the prediction of driving behavior Dynamic Bayesian Networks are used, as it is standing to reason that the future position and velocity are dependent on the actual or preceding position and velocity. One example can be found in [26], where the behavior of the surrounding vehicles was predicted with the Dynamic Bayesian Network in Figure 2.3. In this case the future velocity and lateral displacement were predicted based on the respective preceding values and an indicator signal.…”

Section: Special Characteristics Of Bayesian Networkmentioning

confidence: 99%

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Probabilistic Prediction of Driving Behavior on Country Roads

Sovtic

Adelberger

Wang

2022

2022 European Control Conference (ECC)

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Advanced Driver Assistance Systems (ADAS) are already an important contributor to safety in conventional vehicles and thus in road traffic. This is obviously necessary, as in Austria in 2020, almost 99% of all accidents with personal injury were presumably caused by misconduct of involved persons or impairments [1]. Furthermore, country roads seem to be a safety-relevant environment, as they account for more than 50% of the fatalities in road traffic in Austria.Im nächsten Schritt wird der Einfluss der Identifikationsstrategie auf die Prädiktionsergebnisse betrachtet, wobei für die Identifikation ein Maß für die "Reichhaltigkeit" eines Trainingsdatensatzes eingeführt wird. Es zeigt sich, dass die "Reichhaltigkeit" als auch der Umfang des Identifikationsdatensatzes die Qualität der Prädiktionsmodelle beeinflusst.Schließlich wird ein ausgewähltes Bayes'sches Netzwerk in Simulation für einen Abstandsregeltempomat (ACC) verwendet, um das Verhalten des vorausfahrenden Fahrzeugs vorherzusagen. Der ACC wird als optimal-control Problem formuliert und im Rahmen eines MPC gelöst, während die Validierung auf einem Maß für den Kraftstoffverbrauch und Fahrkomfort basiert. Es zeigt sich bei der Prädiktion mit dem Bayes'schen Netz eine Verbesserung des Kraftstoffverbrauchs und Fahrkomforts im Vergleich zu anderen Prädiktionsmethoden, die keine topologischen Eigenschaften berücksichtigen.

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Section: Structure and Aim Of This Workmentioning

confidence: 99%

Section: Special Characteristics Of Bayesian Networkmentioning

confidence: 99%

Probabilistic Prediction of Driving Behavior on Country Roads

Sovtic

Adelberger

Wang

2022

2022 European Control Conference (ECC)

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“…According to NHTSA statistics [2], 28,190 people died in motor vehicle crashes in the first nine months of 2020. Almost 94% of serious crashes were attributed to driver negligence, such as sleepiness, panic, inexperience, and human reaction time, especially when drivers intended to perform lane changes or to overtake another vehicle, which were the most likely causes of accidents.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, although the optimization problem can be calculated in real time, omitting the key dynamic factors causes a difference between the motion planning and actual vehicle motion. Therefore, a nonlinear MPC has been widely applied for collision avoidance and motion planning [23][24][25][26][27][28][29] owing to its ability to handle nonlinear constraints and to consider a nonlinear vehicle dynamic model. Although nonlinear MPC can handle nonlinear dynamic systems, it requires an iterative solution of optimal control problems on a finite prediction horizon.…”

Section: Introductionmentioning

confidence: 99%

“…Although these methods can increase robustness and efficiency, they tend to be overly conservative or difficult to implement for application in all scenarios. A stochastic MPC is employed to deal with the overtaking maneuvers on country roads using probabilistic traffic predictions [28]. In addition, the behavior of the surrounding vehicles is stochastically predicted using Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

Lin

Huang

et al. 2021

Electronics

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According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity.

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Decision making through stochastic maneuver validation for overtaking on country roads

Adelberger

Wang

2020

2020 59th IEEE Conference on Decision and Control (CDC)

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Autonomous Overtaking Assistant for Country Road Scenarios

Cited by 6 publications

References 16 publications

Probabilistic Prediction of Driving Behavior on Country Roads

Probabilistic Prediction of Driving Behavior on Country Roads

Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

Decision making through stochastic maneuver validation for overtaking on country roads

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