Overview
Wayve ikambani yeUK inovaka masisitimu ekuzvityaira ane imwechete yakadzidza neural network inomepu mapikseli ekamera zvakananga kune zvidhiraivho zvekutyaira - hapana mitemo yakadzorwa nemaoko kana mamepu eHD. Izvo zvine basa nekuti iyi yekupedzisira-kusvika-kumagumo nzira inovimbisa mota dzinouya kumaguta matsva pasina inodhura remaping.
Wayve uye End-to-End Driving Models inonzwisiswa zvakanyanya mumamiriro ehurongwa, kuwana modhi, sarudzo dzepuratifomu, uye ecosystem kudyidzana.
Deep Dive
Yakavambwa muCambridge muna 2017, Wayve inoramba iyo yechinyakare yekuzvityaira yekubika yemamodule akaparadzana ekuona, kufanotaura, uye kuronga zvakanamirwa pamwechete nekodhi yakanyorwa nemaoko. Pane kudaro, inodzidzisa imwe hombe neural network kupera-kusvika-kumagumo: vhidhiyo kubva kumakamera asingadhure inopinda mukati, kutungamira uye kukurumidza kunobuda, yakadzidzwa kubva mukuratidzira kwekutyaira kwevanhu. Wayve nemukurumbira inodzivirira inodhura LiDAR uye pre-yakavakwa HD mepu, kubheja kuti kudzidza kunogadzirisa maitiro anoita vatyairi vevanhu. Yayo GAIA-1 uye gare gare GAIA-2 mamodheru epasirese anoteedzera echokwadi kutyaira vhidhiyo kudzidzisa uye kuyedza mutemo. Muna 2024 Wayve yakakwidza mari inopfuura bhiriyoni imwe yemadhora inotungamirwa neSoftBank, Nvidia, uye Microsoft, uye yakaedza mota mumaguta mazhinji eUK ndokutanga kuwedzera kuUS neJapan.
Technical Insight
Kupera-kusvika-kumagumo kudzidza kunotsiva modular mapaipi netiweki inopatsanurika yakadzidziswa nekutevedzera kudzidza pakutyaira kwevanhu, kazhinji inonatswa nekusimbisa kudzidza. Mamodheru epasirese eWayve akaita seGAIA-2 mamodheru evhidhiyo anofanotaura mafaera eramangwana akamisikidzwa pane zviito, zvichiita kuti timu ibudise zvisingawanzoitiki (jaywalkers, fog) zvakachipa mukuenzanisa. Rutivi rwepafiripi rwunodudzira: imwe bhokisi dema mutemo wakaoma kugadzirisa uye kupa certificate pane pombi apo imwe neimwe module inogona kuongororwa.
Mastering Wayve uye End-to-End-Ekupedzisira Driving Models
Wayve ikambani yeUK inovaka masisitimu ekuzvityaira ane imwechete yakadzidza neural network inomepu mapikseli ekamera zvakananga kune zvidhiraivho zvekutyaira - hapana mitemo yakadzorwa nemaoko kana mamepu eHD. Izvo zvine basa nekuti iyi yekupedzisira-kusvika-kumagumo nzira inovimbisa mota dzinouya kumaguta matsva pasina inodhura remaping. Wayve uye End-to-End Driving Models inonzwisiswa zvakanyanya mumamiriro ehurongwa, kuwana modhi, sarudzo dzepuratifomu, uye ecosystem kudyidzana. Kuvaka kunzwisisa kwakadzama, kubata Wayve uye End-to-End Driving Models semuenzaniso wekushanda, kwete chinhu chimwe chete: tsanangura zvinodikanwa, kujekesa fungidziro, uye patsanura izvo system inogona kuita yakavimbika kubva kune ichiri kuda kutonga nyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Wayve uye End-to-End Driving Models inoongorora zano revatengesi, kuvimbika kwemepu yemugwagwa, uye njodzi yekuvhara isati yaita. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Mamepu emigwagwa emutengesi anopesvedzera izvo izvo timu yako inogona kugadzira inotevera. Panguva imwecheteyo, zviziviso zveLaunch zvinogona kupfuura kugadzikana mune chaiyo yekugadzira workflows. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Mamepu emigwagwa emutengesi anopesvedzera izvo izvo timu yako inogona kugadzira inotevera.
Mamepu emigwagwa emutengesi anopesvedzera izvo izvo timu yako inogona kugadzira inotevera. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Mamiriro ezvekutengeserana uye sarudzo dzekuendesa dzinokanganisa mutengo wenguva refu uye njodzi.
Mamiriro ezvekutengeserana uye sarudzo dzekuendesa dzinokanganisa mutengo wenguva refu uye njodzi. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Kambani inokurudzira inogadzirisa kusarudzika kwechigadzirwa, mamiriro ekuchengetedza, uye kuvhurika.
Kambani inokurudzira inogadzirisa kusarudzika kwechigadzirwa, mamiriro ekuchengetedza, uye kuvhurika. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
Mepu-isina kutyaira mudhorobha mumaguta asina kujairika eUK uchishandisa chete kupinza kamera uye mutemo wakadzidzwa
GAIA-2 yepasirese modhi inogadzira synthetic edge-kesi vhidhiyo (mabhasikoro, mamiriro ekunze) kushushikana-kuyedza network yekutyaira.
Kupa marezenisi AV2.0 software kune vanogadzira mota saka aripo emota kamera masutu anowana yepamberi yakabatsirwa kutyaira
Fleet kudzidza uko data kubva kumota zhinji dzinofambiswa nevanhu inovandudza imwe yakagovaniswa neural kutyaira modhi
Maitiro Ekuita
Wayve uye End-to-End Driving Models mukuita
Mepu-isina kutyaira mudhorobha mumaguta asina kujairika eUK uchishandisa chete kupinza kamera uye mutemo wakadzidzwa.
Kutyaira kwemudhorobha kusina mepu mumaguta asina kujairika eUK uchishandisa chete kupinza kamera uye mutemo wakadzidzwa Zvikwata zvinowanzowana mibairo iri nani pazvinenge zvatsanangudza zvikumbaridzo zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Wayve uye End-to-End Driving Models mukuita
GAIA-2 yepasirese modhi inogadzira inogadzirwa edge-kesi vhidhiyo (mabhasikoro, mamiriro ekunze) kushushikana-kuyedza network yekutyaira.
GAIA-2 yepasirese modhi inogadzira inogadzirwa edge-kesi vhidhiyo (mabhasikoro, mamiriro ekunze) kushushikana-kuyedza yekutyaira network Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye kukanganisa mutengo nekufamba kwenguva.
Wayve uye End-to-End Driving Models mukuita
Kupa marezenisi AV2.0 software kune vanogadzira mota saka aripo emota kamera masutu anowana yepamberi yakabatsirwa kutyaira.
Kupa marezenisi AV2.0 software kune vanogadzira mota saka varipo vemota kamera masutu vanowana yepamberi yakabatsirwa kutyaira Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Wayve uye End-to-End Driving Models mukuita
Fleet kudzidza uko data kubva kumota zhinji dzinofambiswa nevanhu inovandudza imwe yakagovaniswa neural kutyaira modhi.
Fleet kudzidza uko data kubva kumota zhinji dzinotyairwa nevanhu inovandudza imwe yakagovaniswa neural yekutyaira modhi Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
Zviziviso zvekutanga zvinogona kupfuura kugadzikana mune chaiyo yekugadzira workflows.
Mitengo yeAPI kana shanduko yepolicy inogona kukanganisa fungidziro husiku.
Kutsamira kune mumwe-mutengesi kunowedzera kukiya-mukati uye mari yekufambisa.
Implementation Roadmap
Ongorora vanopa uchishandisa ako ega mabasa uye dataset.
Ongorora vanopa uchishandisa ako ega mabasa uye dataset. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Wongorora zvakavanzika, chengetedzo, uye mazwi emutemo usati wabatanidzwa.
Wongorora zvakavanzika, chengetedzo, uye mazwi emutemo usati wabatanidzwa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chengetedza chirongwa chekudzokera kumashure kune mamodheru kana vatengesi.
Chengetedza chirongwa chekudzokera kumashure kune mamodheru kana vatengesi. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Tarisa zvinyorwa zvekuburitsa kuitira kuti shanduko yemigwagwa isashamise zvikwata.
Tarisa zvinyorwa zvekuburitsa kuitira kuti shanduko yemigwagwa isashamise zvikwata. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.