Systematic Comparison of Supervised Learning Methods to Reduce Calibration Effort in Engine Control Development

Complexity of engine control systems is continuously growing due to an increased number of subsystems and the need for robust performance. For traditional map-based as well as state-of-the-art model-based approaches, this will lead to unacceptable development costs and time for future engines. Parametrization of the embedded models using...

Long Horizon Risk-Averse Motion Planning: A Model-Predictive Approach

Developing safe automated vehicles that can be proactive, safe, and comfortable in mixed traffic requires improved planning methods that are risk-averse and that account for predictions of the motion of other road users. To consider these criteria, in this article, we propose a nonlinear model-predictive trajectory generator scheme, which...

Informed sampling-based trajectory planner for automated driving in dynamic urban environments

The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners...

Scenario-based Evaluation of Prediction Models for Automated Vehicles

To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement...

Detection and Mitigation of Functional Insufficiencies in Autonomous Vehicles: The Safety Shell

Autonomous vehicles (AVs) promise to reduce greenhouse gas emissions, increase comfort and throughput of transportation, while simultaneously significantly reducing traffic deaths. To allow autonomous vehicles to satisfy the high safety levels for unsupervised participation in realistic traffic, the faults and functional insufficiencies of AV...

Evaluating the Quality of Lane Change Event Detection: Effect of Situational Variables

To develop safe automated driving functions, knowing road-user’s lane change behaviour is critical. This detection problem may depend on multiple aspects such as road conditions, location, and weather. To understand the effect of these situational variables, this work introduces a lane change detection algorithm and assessed its performance...

Situation-Aware Drivable Space Estimation for Automated Driving

An automated vehicle (AV) must always have a correct representation of the drivable space to position itself accurately and operate safely. To determine the drivable space, current research focuses on single sources of information, either using pre-computed high-definition maps, or mapping the environment online with sensors such as LiDARs or...

Anomaly Detection from Cyber Threats via Infrastructure to Automated Vehicle

Using Infrastructure-to-Vehicle (I2V) information can be of great benefit when driving autonomously in highdensity traffic situations with limited visibility, since the sensing capabilities of the vehicle are enhanced by external sensors. In this research, a method is introduced to increase the vehicle’s self-awareness in intersections for one...

D3.3 Vehicle demonstrator for trajectory planning and control for combined automatic emergency braking and steering manoeuvres including system for VRU detection, motion planning and trajectory control to enhance real world performance

This Deliverable falls under the SAFE-UP Project Work Package 3 “Active safety systems for vehicle-VRU interaction” and specifically under the Task 3.4 “Advanced intervention functions to avoid critical events”. It is a purely technical document that targets to support the efficient monitoring of the technical developments for Demonstrator 3 ...

Electric Powertrain Topology Analysis and Design for Heavy-Duty Trucks

Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby,...

Collision-Free Trajectory Planning With Deadlock Prevention: An Adaptive Virtual Target Approach

Most human-centred robotic applications require robots to follow a certain pre-defined path. This makes the robot’s autonomous movements acceptable and predictable for humans. Planning a trajectory for the robot thus involves guiding it along this desired path. The classical approach of segmenting a path into multiple waypoints and tracking them...

A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction

Vehicle-to-vehicle communication is a solution to improve the quality of on-road traveling in terms of throughput, safety, efficiency and comfort. However, road users that do not communicate their planned activities can create dangerous situations, so prediction models are needed to foresee and anticipate their motions in the drivable space....

Robust model predictive cooperative adaptive cruise control subject to V2V impairments

To improve traffic throughput, Cooperative Adaptive Cruise Control (CACC) has been proposed as a solution. The usage of Vehicle-to-Vehicle (V2V) communication enables short following distances, thereby increasing road capacity and fuel reduction (especially for trucks). Control designs for CACC use the wirelessly communicated intended...

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are obtained, which go beyond the results achieved with...

Effects of using Synthesized Driving Cycles on Vehicle Fuel Consumption

Creating a driving cycle (DC) for the design and validation of new vehicles is an important step that will influence the efficiency, functionality and performance of the final systems. In this work, a DC synthesis method is introduced, based on multi-dimensional Markov Chain, where both the velocity and road slope are investigated. Particularly,...

Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well. Driven by strict emission regulations and tight fuel targets, hybrid or electric vehicle manufacturers aim to develop new and more energy- and cost-efficient...