Please enable JavaScript.

Coggle requires JavaScript to display documents.

Impact analysis - Coggle Diagram

- - - - Roadside infrastructure based aubmented perception
        
        Duration of perception detection: much long for urbain scenario (20 seconds) and much shorter for highway scenarios (5 seconds,10 seconds) L5.2 Fig; 2.2
        
        Further solutions to increase the duration of vehicle detection and coverage area are useful.
        
        perception distnace: Local perception 70 m for LCA-B and LCA-T. 270 m for LMA-M Comm: 97.5 m for
        V2X: 97.5 for LCA-B, 1050 for LCA-T, 1430m for LMA-M
        
        Due to reduced sensing area, for a given detected object, the contirution of V2X is much higher than the local perception, especially in highway area 100% vs 15%). The contribution of local perception can increase up 60% vs. 100%.
        
        localisatione error can be up to few meters for perception and it can reach 20 meter for V2X. The reasons Are GPS error for V2X and precesition of roadside sensor error. Sensor instability due to wind, or imprice calbration can be the main issues.
      - Extended services for connected and automated vehicles
        
        number of objects in a CPM can be 5 for LCA-B and 20 for LMA-M. CPM update period is observed between 0.2s and 0.75s.
        
        multi-sensor system is necessary to efficienty cover the target road section.
        
        object classification is imporatnt for collisiona avoidance in urban area.
      - assistance for lane changing manoeuvres in urban area and highway LCA-B, LCA-T et LMA-M
        
        Prerequisite for good collision risk analysis
        
        sensor accuracy
        
        In PACV2x, we observed that camera placed high on the poles were oscillating with the wind which caused an obvious error of object positioning, especially at far.
        
        We suggest to way to fix that for future usage: either camera must be mounted on very rigid structure, or future works should envisage online extrinsic calibration.
        
        sensor data frequency
        
        High impact of calbration and low camera frquency on the precision of the prediction
        
        quality fo dat fusion
        
        quality of the prediction algorithm: PaCV2X approach was standard kinematics strategy
        
        observed increasing error in the horizon of predictions
        
        Specifically, in end-to-end applications deep networks have shown the ability of doing significantly more accurate prediction than traditional probabilistic or credibilistic fusions.
      - System architecture
        
        Edge computing architecture
        
        Fluctuation in the qulaity of the VPN connection through the 4G cellular connection.
        
        Rising ambient temperature in CPU housing enchlosures.
        
        distributed architecture on WAN, where the calculation are made in the cloud
        
        the rason is safety of raod usis, operatory, and experimental equipmetns;
        
        Reducing performance for updating data between camereas, CPU and RSUs
        
        24/7 availiabyt, enought bandwidht to ensure rapied and regulat transit of snsor stream in toer to maintina sufficienty low latency in order to have relevant information availalbe for the processing of use case and able to restore information to connected vehicle in time.
        
        If this is not guarenteed edge computing archtecture shoudl be applied.
    - - Theoretical method using FMECA and TVRA on subset of the PACV2X system architecture: 1) centralised RSU with CV 2) centralised RSU with autonomous vehicle 3) distributed RSU with CVs
        
        Propose countermeasures
        
        Consistency check, plausibility check
        
        TVRA: is centered on human cyberattacks
        
        Five security aobjectives: authentification, availability, confidentiality, integrity, and privacy
        
        threats: interception, manipulation, denial of service, and reputation;
        
        Occurrence likelihood and impacts of the trehats is qualitfied
        
        Risk are then established a set of counter measures ar proposd.
        
        4 more items...
        
        FMECA: cetnered on system compoenent (hardware/software failure)
        
        model the considered system architecture
        
        develpe risk management plan
        
        Conduct a FMECA, identifying the failure mode risks as well as their effects and criticalities.
  - - - capability of creation of minimum gap for insertion
    - - preparing deploymen
      - SWOT (Strenght Weakness Opportunes and threats) L5.3 Chapter2.3
        
        Strength
        
         Aligned with the ETSI standardization work.
        
         Advanced local perception technology using AI.
        
         Consistent perception fusion between local perception and remote perception provided by C-ITS.
        
         Proof of Concept of “Virtual traffic agent” in an open environment.
        
         Satisfactory performances measured and demonstrated. Weakness
        
         Based on non-mature (CPS) standards and on proprietary messages (MCM and TAM).
        
         Small number of cooperative vehicles used, not representative of an operational situation.
        
         No possible validation (for safety reasons) in the full range of vehicles’ velocities.
        
         Limited validation process related to the time and resources availability.
        
         Limit of the experiment period due to COVID 19 disturbances.
        
        Opportunities
        
         Enables efficient French contributions at the level of CEN and ETSI Standard Organizations.
        
         Ready to develop a European FOT for release 2 (automation) ITS deployment.
        
         Increased partners and other actors’ capabilities to develop standard products.
        
         Useable new acquired knowledge and competencies at the level of PAC V2X partners. Threats
        
         Huge competition from other European member states and more generally from USA and Asia.
        
         Multiplicity of available non-interoperable communication technologies and standards.
        
         Difficulties to agree on a balanced business model between the vehicles’ industry and the road infrastructure authorities.
        
         Difficulties to set-up a European organization for ITS life cycle management including the system certification.
        
        Proposing furhter improvemesnt : standardisation organisation, Europoean projcet, exchanges between OEM and ifnra
    - - CPS and MCS advantages and limits L5.3, chapter 3