Advanced Control and Connection Techniques for Autonomous Vehicles

Dear Colleagues,

A large part of the current research on autonomous vehicles concentrates on connectivity and motion planning.

The promise of fully connected vehicles is very challenging for experts, researchers, automakers, and automotive service providers alike. Several frameworks suggest different levels of connectivity, ranging from basic to more complex ones. Overall, connected cars will allow more pleasant and safer driving experiences, smoother traffic flows, lower emissions, and optimized energy consumption.

Motion planning, also known as path or navigation planning, plays a key role in improving autonomy, safety, and comfort, and it has a strong relationship with the vehicle’s connectivity. The level of automation is usually defined within six levels of autonomy, ranging from no driving automation (level 0) to full driving automation (level 5). Even though level 5 vehicles are probably several years away, autonomous transportation systems require motion planning methods to generalize unpredictable situations and to reason promptly and safely across a huge range of different situations.

Although enormous efforts have been made to solve several theoretical and technological challenges in these two areas, several problems remain to be addressed. Many of these remaining issues require computationally intensive algorithms. A solution to this problem is offered by edge computing, which supports more resource capacity on mobile devices, and by highly parallel architecture systems, which are powerful and often underused. In this framework, the decomposition and parallelization of existing algorithms are extremely important tasks.

The purpose of this Special Issue is to focus on the limitations of the computational resources available in onboard computers. Thus, while the reduction of the computational costs of existing algorithms may be a solution, we also concentrate on parallel and edge computation to improve computational power and efficiency.

The Special Issue thus makes specific reference to the following aspects:

• Edge computation for a vehicle’s connectivity and motion planning;
• Parallel processing techniques for a vehicle’s connectivity and motion planning;
• Classification, characterization, and the state of the art of connectivity and path planning methodologies and related algorithms;
• Novel path-planning techniques for autonomous vehicles;
• SIMD (single instruction, multiple data) and SIMT (single instruction, multiple threads) architectures and edge-computing architectures for autonomous vehicles;
• Motion-planning applications;
• Privacy issues and security in edge computing for transportation systems.

Prof. Dr. Stefano Quer
Guest Editor

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• autonomous vehicles 
• connected vehicles 
• autonomous navigation 
• path and motion planning 
• obstacle avoidance 
• vision-aided navigation 
• edge computing architectures for vehicles’ connectivity 
• SIMD (single instruction, multiple data) and SIMT (single instruction, multiple threads) architectures for motion planning 
• new edge-computing architectures for vehicles’ connectivity 
• divide and conquer, optimization, and estimation algorithms for path planning and connectivity